Their powers and actions are governed by the charter. For example the East India Company, Bank of England;etc. Such companies are now treated as foreign companies.Companies may be classified from different(points of view as follows A. Companies According to their Mode of Incorporation. In this point of view companies may bel. Chartered Companies, 2. Statutory Companies, and 3.Companies.1. Chartered Companies. These companies are incorporated under special Royal charter issued by the King or Queen. A company registered under the companies Act is not a chartered company Union of India AIR (/959) Cat. 95].2. Statutory Companies. Companies which are brought into existence and governed by special Acts of the legislature are known as Statutory Com allies. Public utility services, electric supply, gas work, etc, are generall by cnmpames ormed unaer specl11c Ja v WhICll govern and hese Com anies are usuall given some special owers which are not generall ossessed b companies regis ereer Companies Act.. This is necessary because public uti ItIes are mo oly orgamsatIons and supply services which are vital to the needs of the public. The Reserve Bank of India, the L.I.C.
of India, the Unit imes of India, the Indian Airlines and the State Bank of India are some examples of statutory companies in India. The audit of such companies is conducted under the supervision and control of the Auditor General of India. These companies have a corporate existence and can sue or be sued in their own name. 3. Registered Companies. Companies registered under the Companies Act, 1956 or under any previous Companies Act; are
Registered Companies.
Wednesday, December 26, 2007
Friday, December 21, 2007
Classification by Intelligence level and power
Classification by Intelligence level and power
Lee et al. classify Web agents into four levels, according to their
intelligence and power.
1) Level 0 These agents retrieve documents for a user under straight
orders. Popular Web browsers fall into this category.
2) Level 1 These agents provide user initiated searching facility for
finding relevant Web pages.
Internet search agents such as Yahoo, Alta Vista, and
Infoseek are examples.
When the user provides key words, the search engine
matches them against the indexed information.
3) Level 2 These agents maintain user’s profiles. Then they monitor
internet information and notify the users whenever relevant
information is found.
Examples of such agents are WebWatcher and SIFT.
4) Level 3 Agents at this level have learning and educative component
of user profiles to help a user who cannot formalize a query
or specify a target for a search.
DiffAgent and Letzia are examples of such agents.
Classification of application area
According to IBM’s white paper, there are nine major application areas
that elate to internet agents.
1. Agents that assist in workflow and administrative management
2. Agents that collaborate with other agents and people
3. Agents that support electronic commerce
4. Agents that support desktop applications
5. Agents that assist in information access and management
6. Agents that process mail and messages
7. Agents that control and manage the network access
8. Agents that manage systems and networks
9. Agents that create user interfaces
Gilbert and Janca indicates that many agents are being used in each
category, be in mainstream use. They also provide many examples of
agents in each of the nine categories.
King classifies agents into interface, tutors, scheduling assistants,
search agents, report agents, presentation agents, navigation agents,
and role playing agents.
Internet based software agents
Following are the main internet based software agents.
1. E-mail agents
2. Web browsing assisting agents
3. Frequently asked questions agents (FAQ)
4. Intelligent search agents
5. Internet softbot
Managerial Issues
The following are some representative managerial issues.
1) Cost Justification 5) Other ethical issues
2) Security a) Agent learning
3) Privacy b) Agent accuracy
4) Industrial intelligence and ethics c) Heightened expectations
5) Other ethical issues d) System acceptance
e) System technology
f) Agent development toolkits
g) Strategic information systems
Cost Justification
With technology rapidly changing and with intelligent agents in their
developmental infancy, it ma be hard to justify spending lots of money
on something that may be obsolete tomorrow.
Security
Agents are a technology with many unknown ramifications.
With the great concerns about the security the security of systems,
does it make sense that a company would knowingly send out agents
that could come back laden with a virus or hiding a Trojan horse?
Privacy
There are some cases in which agents have intruded on people’s
privacy.
Example _ Cookies
Industrial intelligence and ethics
Legitimate industrial intelligence gathering is usually expensive and
time consuming.
Other ethical issues
Agents represent a significant new way of interacting with the world.
Just as a unique etiquette has evolved on the internet, there will need
to be new definitions of acceptable and unacceptable uses for agents.
Agent learning
The theory behind agents is that the more you use them, the more they
learn and, therefore, the more effective they are.
Agent accuracy
Along with agent learning, comes agent accuracy.
Assuming that the agent develops the ability to learn, the next issue
facing managers is the accuracy of the data returned by the agent.
Heightened expectations
With any new technology or product come higher expectations. This is
especially true when it comes to intelligent agents.
System acceptance
The introduction of intelligent agents into an existing system can
sometimes create problems.
Systems have different architectures and operating systems.
System technology
As intelligent agents become more powerful, the systems required to
run them also must be more powerful.
Agent development toolkits
To implement agent applications, one can use specially toolkits.
Strategic information systems
Management has a great need to survey the environment for
opportunities and treats to the long term survival and success of the
company.
Intelligent agents are an excellent long term strategic asset a company
may develop in gathering data from a variety of sources around the
world.
EDP Electronic Data Processing
UIMS User Interface Management System
GUI Graphical User Interface
IFPS Interactive Financial Planning System
DFPM Educom’s Financial Planning Model
ES Expert System
ANN Artificial Neural Networks
VRML Virtual reality Markup Language
GPS Global Positioning System
GIS Global Information System
OLE Object Linking and Embedding
NGT Nominal Group Technique
SANN Simulated Artificial Neural Networks
Lee et al. classify Web agents into four levels, according to their
intelligence and power.
1) Level 0 These agents retrieve documents for a user under straight
orders. Popular Web browsers fall into this category.
2) Level 1 These agents provide user initiated searching facility for
finding relevant Web pages.
Internet search agents such as Yahoo, Alta Vista, and
Infoseek are examples.
When the user provides key words, the search engine
matches them against the indexed information.
3) Level 2 These agents maintain user’s profiles. Then they monitor
internet information and notify the users whenever relevant
information is found.
Examples of such agents are WebWatcher and SIFT.
4) Level 3 Agents at this level have learning and educative component
of user profiles to help a user who cannot formalize a query
or specify a target for a search.
DiffAgent and Letzia are examples of such agents.
Classification of application area
According to IBM’s white paper, there are nine major application areas
that elate to internet agents.
1. Agents that assist in workflow and administrative management
2. Agents that collaborate with other agents and people
3. Agents that support electronic commerce
4. Agents that support desktop applications
5. Agents that assist in information access and management
6. Agents that process mail and messages
7. Agents that control and manage the network access
8. Agents that manage systems and networks
9. Agents that create user interfaces
Gilbert and Janca indicates that many agents are being used in each
category, be in mainstream use. They also provide many examples of
agents in each of the nine categories.
King classifies agents into interface, tutors, scheduling assistants,
search agents, report agents, presentation agents, navigation agents,
and role playing agents.
Internet based software agents
Following are the main internet based software agents.
1. E-mail agents
2. Web browsing assisting agents
3. Frequently asked questions agents (FAQ)
4. Intelligent search agents
5. Internet softbot
Managerial Issues
The following are some representative managerial issues.
1) Cost Justification 5) Other ethical issues
2) Security a) Agent learning
3) Privacy b) Agent accuracy
4) Industrial intelligence and ethics c) Heightened expectations
5) Other ethical issues d) System acceptance
e) System technology
f) Agent development toolkits
g) Strategic information systems
Cost Justification
With technology rapidly changing and with intelligent agents in their
developmental infancy, it ma be hard to justify spending lots of money
on something that may be obsolete tomorrow.
Security
Agents are a technology with many unknown ramifications.
With the great concerns about the security the security of systems,
does it make sense that a company would knowingly send out agents
that could come back laden with a virus or hiding a Trojan horse?
Privacy
There are some cases in which agents have intruded on people’s
privacy.
Example _ Cookies
Industrial intelligence and ethics
Legitimate industrial intelligence gathering is usually expensive and
time consuming.
Other ethical issues
Agents represent a significant new way of interacting with the world.
Just as a unique etiquette has evolved on the internet, there will need
to be new definitions of acceptable and unacceptable uses for agents.
Agent learning
The theory behind agents is that the more you use them, the more they
learn and, therefore, the more effective they are.
Agent accuracy
Along with agent learning, comes agent accuracy.
Assuming that the agent develops the ability to learn, the next issue
facing managers is the accuracy of the data returned by the agent.
Heightened expectations
With any new technology or product come higher expectations. This is
especially true when it comes to intelligent agents.
System acceptance
The introduction of intelligent agents into an existing system can
sometimes create problems.
Systems have different architectures and operating systems.
System technology
As intelligent agents become more powerful, the systems required to
run them also must be more powerful.
Agent development toolkits
To implement agent applications, one can use specially toolkits.
Strategic information systems
Management has a great need to survey the environment for
opportunities and treats to the long term survival and success of the
company.
Intelligent agents are an excellent long term strategic asset a company
may develop in gathering data from a variety of sources around the
world.
EDP Electronic Data Processing
UIMS User Interface Management System
GUI Graphical User Interface
IFPS Interactive Financial Planning System
DFPM Educom’s Financial Planning Model
ES Expert System
ANN Artificial Neural Networks
VRML Virtual reality Markup Language
GPS Global Positioning System
GIS Global Information System
OLE Object Linking and Embedding
NGT Nominal Group Technique
SANN Simulated Artificial Neural Networks
Classification and Types of Agents
Classification and Types of Agents
There are several types of agents that can be classified in different
ways.
Franklin and Graesser use a taxonomic tree to classify autonomous
agents.
Relevant to managerial decision making are computational agents,
software agents and risk specific agents.
Another classification is according to control structure, computational
environment, programming language and application type.
Classification by Classification by characteristics
1) Organizational and personal agents 1) Agency
2) Private agents versus public agents 2) Intelligence
3) Software agents and intelligent agents 3) Mobility
4) Mobile agents
Organizational and personal agents
There are two broad classifications of intelligent agents.
1. Organizational
2. Personal
Organizational agents execute task on behalf of a business process or
computer application.
Personal agents perform tasks on behalf of users.
Example of an organizational intelligent agent is an automatic e-mail
sorting system. When a new message come in, it an automatically be
routed to the right file and folder.
Personal agents are very powerful. They allow users to go directly to
the information they want on the internet.
Private agents versus public agents
A private agent works only for one user who creates it.
Public agents are created by a designer for the use of anybody who
has access to the application, network or database.
Software (simple) agents and intelligent agents
Truly intelligent agent must be able to learn and exhibit autonomy.
Most internet and electronic commerce agent do not exhibit these
characteristics.
Therefore, they are often called software, or simple agents.
Mobile Agents
Mobile agents can move from one internet site to another and send
and retrieve data to the user, who can focus on other work in the
meantime.
This can be very helpful to a user.
Software applications that automatically watch stocks are available, but
they require a dedicated line and computer.
The mobile agent travels from site to site, looking for information on
that stock as instructed by the user.
Example _ if the stock price hits a certain level or if there is news about
the stock, the agent alerts the user.
Classification by characteristics
Of the various characteristics of agents three are of special importance.
Agency, Intelligence and mobility.
Agency
This is the edge of autonomy and authority vested in the agent and can
be measured at least qualitatively by the nature of the interaction
between the agent and other entities in the system.
The degree of agency is enhanced if an agent represents a user in
some way.
Intelligence
This is the degree of reasoning and learned behavior.
The agent’s ability to accept the user’s statement of goals and carry out
the tasks delegated to it.
Mobility
This is the degree to which agents themselves travel through the
network.
Some agents may be static, either residing on the client machine or
initiated at the server.
Mobile scripts may be composed on one machine and shipped to
another for execution in a suitable secure environment, in this case, the
program travels before execution, so no state data need be attached.
There are several types of agents that can be classified in different
ways.
Franklin and Graesser use a taxonomic tree to classify autonomous
agents.
Relevant to managerial decision making are computational agents,
software agents and risk specific agents.
Another classification is according to control structure, computational
environment, programming language and application type.
Classification by Classification by characteristics
1) Organizational and personal agents 1) Agency
2) Private agents versus public agents 2) Intelligence
3) Software agents and intelligent agents 3) Mobility
4) Mobile agents
Organizational and personal agents
There are two broad classifications of intelligent agents.
1. Organizational
2. Personal
Organizational agents execute task on behalf of a business process or
computer application.
Personal agents perform tasks on behalf of users.
Example of an organizational intelligent agent is an automatic e-mail
sorting system. When a new message come in, it an automatically be
routed to the right file and folder.
Personal agents are very powerful. They allow users to go directly to
the information they want on the internet.
Private agents versus public agents
A private agent works only for one user who creates it.
Public agents are created by a designer for the use of anybody who
has access to the application, network or database.
Software (simple) agents and intelligent agents
Truly intelligent agent must be able to learn and exhibit autonomy.
Most internet and electronic commerce agent do not exhibit these
characteristics.
Therefore, they are often called software, or simple agents.
Mobile Agents
Mobile agents can move from one internet site to another and send
and retrieve data to the user, who can focus on other work in the
meantime.
This can be very helpful to a user.
Software applications that automatically watch stocks are available, but
they require a dedicated line and computer.
The mobile agent travels from site to site, looking for information on
that stock as instructed by the user.
Example _ if the stock price hits a certain level or if there is news about
the stock, the agent alerts the user.
Classification by characteristics
Of the various characteristics of agents three are of special importance.
Agency, Intelligence and mobility.
Agency
This is the edge of autonomy and authority vested in the agent and can
be measured at least qualitatively by the nature of the interaction
between the agent and other entities in the system.
The degree of agency is enhanced if an agent represents a user in
some way.
Intelligence
This is the degree of reasoning and learned behavior.
The agent’s ability to accept the user’s statement of goals and carry out
the tasks delegated to it.
Mobility
This is the degree to which agents themselves travel through the
network.
Some agents may be static, either residing on the client machine or
initiated at the server.
Mobile scripts may be composed on one machine and shipped to
another for execution in a suitable secure environment, in this case, the
program travels before execution, so no state data need be attached.
Supports conditional processing
Supports conditional processing
Many agents use rule based pattern matching logic to make decisions
in the face of changing context.
As agents become more sophisticated, these rules will increasingly be
able to be stated at a higher level, even natural language.
Learning
Some of the newer internet search engines boast intelligent agents,
which can learn from previous requests the user has made.
Reactivity
Agents perceive their environment which may be the physical world, a
user via graphical user interface, a collection of other agents, the
internet, or perhaps all of these combined and respond in a timely
fashion to changes that occur in it.
Proactive ness
Agents do not simply act in response to their environment. They are
able to exhibit goal defected behavior by taking initiative.
Temporal continuity
The agent should be a continuously running process, not a one shot
deal that terminates after completing a series of commands.
Personality
For an agent to be effectively, it must be believable and be able to
interact with human users.
Mobility
An agent that can transport it across different system architectures and
platforms is far superior to those that cannot.
Why intelligent Agents.
Information access and navigation are today’s major applications of
intelligent agents, but there are several other reasons why this
technology is expected to grow rapidly.
Examples _ Intelligent agents can improve computer network security,
support electronic commerce, empower employees, and increase
productivity and quality
Following are the reasons.
1) Decision Support 4) Search and retrieval
2) Repetitive office activity 5) Domain experts
3) Mundane personal activity
Decision support
There is a need for increased support for tasks performed by
knowledge workers, especially in the decision machine area.
Timely and knowledgeable decisions businesses in the marketplace.
Repetitive office activity
There is a pressing need to automate tasks performed by
administrative and clerical personnel in functions such as sales or
customer support to reduce labor costs and increase office productivity.
Mundane personal activity
In a fast paced society, time strapped people need new ways to
minimize the time spent on routine personal tasks such as booking
airline tickets so that the voice activated interface Aetna that reduces
the burden on the user of having to explicitly command the computer.
Another is that of a personal assistant, whom learns our work patterns
and replicates the on typical but mundane tasks such as appointments,
meal reservations, and e mail processing.
Search and retrieval
It is not possible to directly manipulate a distributed database system in
an electronic commerce setting with millions of data objects.
These agents perform the tedious, time consuming and repetitive tasks
of searching databases, retrieving and filtering information and
delivering it back to the user.
Domain experts
It is advisable to model costly expertise and make it widely available.
Examples of expert software agents could be models of real world
agents such as translators, lawyers, diplomats, union negotiators,
stockbrokers, and even clergy.
Many agents use rule based pattern matching logic to make decisions
in the face of changing context.
As agents become more sophisticated, these rules will increasingly be
able to be stated at a higher level, even natural language.
Learning
Some of the newer internet search engines boast intelligent agents,
which can learn from previous requests the user has made.
Reactivity
Agents perceive their environment which may be the physical world, a
user via graphical user interface, a collection of other agents, the
internet, or perhaps all of these combined and respond in a timely
fashion to changes that occur in it.
Proactive ness
Agents do not simply act in response to their environment. They are
able to exhibit goal defected behavior by taking initiative.
Temporal continuity
The agent should be a continuously running process, not a one shot
deal that terminates after completing a series of commands.
Personality
For an agent to be effectively, it must be believable and be able to
interact with human users.
Mobility
An agent that can transport it across different system architectures and
platforms is far superior to those that cannot.
Why intelligent Agents.
Information access and navigation are today’s major applications of
intelligent agents, but there are several other reasons why this
technology is expected to grow rapidly.
Examples _ Intelligent agents can improve computer network security,
support electronic commerce, empower employees, and increase
productivity and quality
Following are the reasons.
1) Decision Support 4) Search and retrieval
2) Repetitive office activity 5) Domain experts
3) Mundane personal activity
Decision support
There is a need for increased support for tasks performed by
knowledge workers, especially in the decision machine area.
Timely and knowledgeable decisions businesses in the marketplace.
Repetitive office activity
There is a pressing need to automate tasks performed by
administrative and clerical personnel in functions such as sales or
customer support to reduce labor costs and increase office productivity.
Mundane personal activity
In a fast paced society, time strapped people need new ways to
minimize the time spent on routine personal tasks such as booking
airline tickets so that the voice activated interface Aetna that reduces
the burden on the user of having to explicitly command the computer.
Another is that of a personal assistant, whom learns our work patterns
and replicates the on typical but mundane tasks such as appointments,
meal reservations, and e mail processing.
Search and retrieval
It is not possible to directly manipulate a distributed database system in
an electronic commerce setting with millions of data objects.
These agents perform the tedious, time consuming and repetitive tasks
of searching databases, retrieving and filtering information and
delivering it back to the user.
Domain experts
It is advisable to model costly expertise and make it widely available.
Examples of expert software agents could be models of real world
agents such as translators, lawyers, diplomats, union negotiators,
stockbrokers, and even clergy.
Artificial Neural Networks
Artificial Neural Networks
An artificial neural network is a model that emulates a biological neural
network.
The concepts are used to implement software simulations of the
massively parallel process that involve processing elements (also
called artificial neurons or neuroses) interconnected in network
architecture.
The artificial neuron receives inputs that are analogous to the
electrochemical impulses that the dendrites of biological neurons
receive Ron other neurons.
The output of the artificial signals can be neuron corresponds to signals
sent out from a biological neuron over its axon.
Learning
An ANN learns from its experiences.
The usual process of learning involves three tasks.
1. Compute outputs
2. Compare outputs with desired targets
3. Adjust the weights and repeat the process
Neural Network Hardware
Most current neural network applications involve software simulations
that run on conventional sequential process (called simulated artificial
neural networks, or SANN).
To increase the computational speed when regular computers are
used, one of four approaches is applicable.
1. Faster general purpose computers
2. General purpose parallel processors
3. Neural chips
4. Acceleration boards
Faster general purpose computers
Use a high end PC with a fast Pentium, P6 or PowerPc processor chip
or a RISC workstation like an IBM RS/6000.
General purpose parallel processor
There are a number of general purpose parallel processors, ranging
from the large grained IMB SP – 2 to small grained transcomputers
such as INMOS Transputer.
Neural computing is inherently parallel in nature.
There are a number of parallel neural network codes such as Nnets
from Cosmic / The University of Georgia.
Neural chips
Most of today’s special chips can execute computations very fast, but
they cannot be used to train the network.
The idea of a chip is to provide implementation of neural network data
structures on the following.
An Analog chip _ such as Intel 80170NX Electronically Trainable ANN,
or ETANN
A Digital chip _ such as Micro Devices MD – 1220
An optical chip.
Acceleration boards
These are powerful, dedicated parallel / array processors that can be
installed in regular computers, similar to a math coprocessor.
Such a processor can be thousand soft times faster than a Pentium
processor.
Some examples are….
Adaptive Solutions CNAPS/PC–128 – Which BrainMakers uses
The Mosaic QED Board The HNC Balboa Board
Acceleration bards are extremely useful because they reduce training
time, which is usually quite long.
Intelligent Agents and Creativity
An intelligent Agent (IA) is a computer program that helps a user with
routine computer tasks.
The first vignette is an example of a first generation individually
operated agent.
They can overcome the most critical limitation of the internet – the
information overflow – and make electronic commerce a viable
organization tool.
Names
Several names are used to describe intelligent agents, including
software agnates, wizards, knowbots, and softbots (intelligent software
robots).
The names sometimes reflect the nature of the agent.
Characteristics of Intelligent Agents
Autonomy Learning
Operates in background Reactivity
Single task Proactive ness
Communication Temporal continuity
Automates repetitive tasks Personality
Supports conditional processing Mobility
Autonomy
An agent is autonomous; that is, it is capable of action on its own.
An agent must be able to make some decisions on its own by being
goal oriented, collaborative, and flexible.
It must b able to alter its course or behavior when it meets obstacles to
find ways around the impediment.
Example _ an agent should be able to accept high level request and
decide on its own where and how to carry out the request.
Autonomy implies that an agent takes initiative and exercises control
over its own actions in the following ways.
Goal oriented Accepts high level requests indicating what a human wants and
is responsible for deciding how and where to satisfy certain
requests.
Collaborative Does not blindly bet commands, but can modify requests, ask
clarification questions, or even refuse to satisfy certain requests.
Flexible Actions are not scripted, able to dynamically choose which
actions to invoke and it what sequence, in response to the state
of its external environment.
Self Starting Unlike standard programs directly invoked by user, an agent can
sense changes of its environment and decide when to act.
Operates in backboard
An agent must be able to work out of sight, within the realm of
cyberspace or other computer systems, without the constant attention
of its user.
Some developers use the term remote execution or mobile agents to
describe this attribute.
Single task
In most cases an agent is designed to accomplish a single task.
A single task cold is searching the internet for articles.
Another might be filtering electronic mail.
The more futuristic agents appear capable of doing multiple things.
Communication
Many agents are designed to interact with other agents, humans, or
software programs.
Instead of making a single agent smarter, additional agents can be
created to handle undefeated tasks.
The ability to collaborate enables the system to accomplish more
complex tasks.
Automates repetitive tasks
An agent is designed to do narrowly defined tasks, which it can do over
and over without getting bored or sick or going on strike.
An artificial neural network is a model that emulates a biological neural
network.
The concepts are used to implement software simulations of the
massively parallel process that involve processing elements (also
called artificial neurons or neuroses) interconnected in network
architecture.
The artificial neuron receives inputs that are analogous to the
electrochemical impulses that the dendrites of biological neurons
receive Ron other neurons.
The output of the artificial signals can be neuron corresponds to signals
sent out from a biological neuron over its axon.
Learning
An ANN learns from its experiences.
The usual process of learning involves three tasks.
1. Compute outputs
2. Compare outputs with desired targets
3. Adjust the weights and repeat the process
Neural Network Hardware
Most current neural network applications involve software simulations
that run on conventional sequential process (called simulated artificial
neural networks, or SANN).
To increase the computational speed when regular computers are
used, one of four approaches is applicable.
1. Faster general purpose computers
2. General purpose parallel processors
3. Neural chips
4. Acceleration boards
Faster general purpose computers
Use a high end PC with a fast Pentium, P6 or PowerPc processor chip
or a RISC workstation like an IBM RS/6000.
General purpose parallel processor
There are a number of general purpose parallel processors, ranging
from the large grained IMB SP – 2 to small grained transcomputers
such as INMOS Transputer.
Neural computing is inherently parallel in nature.
There are a number of parallel neural network codes such as Nnets
from Cosmic / The University of Georgia.
Neural chips
Most of today’s special chips can execute computations very fast, but
they cannot be used to train the network.
The idea of a chip is to provide implementation of neural network data
structures on the following.
An Analog chip _ such as Intel 80170NX Electronically Trainable ANN,
or ETANN
A Digital chip _ such as Micro Devices MD – 1220
An optical chip.
Acceleration boards
These are powerful, dedicated parallel / array processors that can be
installed in regular computers, similar to a math coprocessor.
Such a processor can be thousand soft times faster than a Pentium
processor.
Some examples are….
Adaptive Solutions CNAPS/PC–128 – Which BrainMakers uses
The Mosaic QED Board The HNC Balboa Board
Acceleration bards are extremely useful because they reduce training
time, which is usually quite long.
Intelligent Agents and Creativity
An intelligent Agent (IA) is a computer program that helps a user with
routine computer tasks.
The first vignette is an example of a first generation individually
operated agent.
They can overcome the most critical limitation of the internet – the
information overflow – and make electronic commerce a viable
organization tool.
Names
Several names are used to describe intelligent agents, including
software agnates, wizards, knowbots, and softbots (intelligent software
robots).
The names sometimes reflect the nature of the agent.
Characteristics of Intelligent Agents
Autonomy Learning
Operates in background Reactivity
Single task Proactive ness
Communication Temporal continuity
Automates repetitive tasks Personality
Supports conditional processing Mobility
Autonomy
An agent is autonomous; that is, it is capable of action on its own.
An agent must be able to make some decisions on its own by being
goal oriented, collaborative, and flexible.
It must b able to alter its course or behavior when it meets obstacles to
find ways around the impediment.
Example _ an agent should be able to accept high level request and
decide on its own where and how to carry out the request.
Autonomy implies that an agent takes initiative and exercises control
over its own actions in the following ways.
Goal oriented Accepts high level requests indicating what a human wants and
is responsible for deciding how and where to satisfy certain
requests.
Collaborative Does not blindly bet commands, but can modify requests, ask
clarification questions, or even refuse to satisfy certain requests.
Flexible Actions are not scripted, able to dynamically choose which
actions to invoke and it what sequence, in response to the state
of its external environment.
Self Starting Unlike standard programs directly invoked by user, an agent can
sense changes of its environment and decide when to act.
Operates in backboard
An agent must be able to work out of sight, within the realm of
cyberspace or other computer systems, without the constant attention
of its user.
Some developers use the term remote execution or mobile agents to
describe this attribute.
Single task
In most cases an agent is designed to accomplish a single task.
A single task cold is searching the internet for articles.
Another might be filtering electronic mail.
The more futuristic agents appear capable of doing multiple things.
Communication
Many agents are designed to interact with other agents, humans, or
software programs.
Instead of making a single agent smarter, additional agents can be
created to handle undefeated tasks.
The ability to collaborate enables the system to accomplish more
complex tasks.
Automates repetitive tasks
An agent is designed to do narrowly defined tasks, which it can do over
and over without getting bored or sick or going on strike.
Problems and Limitations of Expert System
Problems and Limitations of Expert System
1. Knowledge is not always readily available
2. Expertise an be hard to extract from humans
3. The approach of each expert to situation assessment may be different, yet
correct.
4. It is hard, even for a highly skilled expert, to abstract good situational
assessments when he or she is under time pressure.
5. Users of expert systems have natural cognitive limits.
6. ES work well only in a narrow domain of knowledge
7. Most experts have to independent means of checking whether their
conclusions are reasonable
8. The vocabulary or jargon, that experts use for expressing facts and relations
is often limited and not understood by others.
9. Help is often required from knowledge engineers who are rare and expensive,
a fact that cold make ES construction costly.
10. Lack of trust by end users may be a barrier to ES use.
11. Knowledge transfer is subject to a host of perceptual and judgmental biases.
Cutting Edge DSS Technology : Neural Computing and Intelligent
Agents
Machine learning methods
Machine learning methods and algorithms have been developed.
Following are some of the examples.
Neural
Computing
This approach can be used for inferencing knowledge acquisition, thus, it can
be used for decision support.
Inductive
learning
This approach is used in knowledge acquisition, as in rule induction.
Case based
reasoning and
analogical
Reasoning
This approach is used in knowledge acquisition and inferencing.
Genetic
algorithms
Genetic algorithms attempt to follow some of the procedures of biological
systems, which are excellent learners.
Statistical
methods
Statistical methods have been applied to knowledge acquisition, forecasting
and problem solving.
Explanation
based learning
This approach assumes that there is enough existing theory to explain why
one instance is or is no a prototypical member of a class.
Neural Computing
A different approach to intelligent system involves constructing
computers with architectures and processing capabilities that mimic
certain processing capabilities of the human brain.
The results are knowledge representations based on massive parallel
processing, fast retrieval of large amounts of information and the ability
to recognize pattern based on historical cases.
The technology that attempts to achieve these results is called neural
computing or artificial neural networks (ANNs).
Artificial neural networks are an information processing technology
inspired by studies of the brain and the nervous system.
1. Knowledge is not always readily available
2. Expertise an be hard to extract from humans
3. The approach of each expert to situation assessment may be different, yet
correct.
4. It is hard, even for a highly skilled expert, to abstract good situational
assessments when he or she is under time pressure.
5. Users of expert systems have natural cognitive limits.
6. ES work well only in a narrow domain of knowledge
7. Most experts have to independent means of checking whether their
conclusions are reasonable
8. The vocabulary or jargon, that experts use for expressing facts and relations
is often limited and not understood by others.
9. Help is often required from knowledge engineers who are rare and expensive,
a fact that cold make ES construction costly.
10. Lack of trust by end users may be a barrier to ES use.
11. Knowledge transfer is subject to a host of perceptual and judgmental biases.
Cutting Edge DSS Technology : Neural Computing and Intelligent
Agents
Machine learning methods
Machine learning methods and algorithms have been developed.
Following are some of the examples.
Neural
Computing
This approach can be used for inferencing knowledge acquisition, thus, it can
be used for decision support.
Inductive
learning
This approach is used in knowledge acquisition, as in rule induction.
Case based
reasoning and
analogical
Reasoning
This approach is used in knowledge acquisition and inferencing.
Genetic
algorithms
Genetic algorithms attempt to follow some of the procedures of biological
systems, which are excellent learners.
Statistical
methods
Statistical methods have been applied to knowledge acquisition, forecasting
and problem solving.
Explanation
based learning
This approach assumes that there is enough existing theory to explain why
one instance is or is no a prototypical member of a class.
Neural Computing
A different approach to intelligent system involves constructing
computers with architectures and processing capabilities that mimic
certain processing capabilities of the human brain.
The results are knowledge representations based on massive parallel
processing, fast retrieval of large amounts of information and the ability
to recognize pattern based on historical cases.
The technology that attempts to achieve these results is called neural
computing or artificial neural networks (ANNs).
Artificial neural networks are an information processing technology
inspired by studies of the brain and the nervous system.
Benefits of Expert System
Benefits of Expert System
1. Increased output and productivity
ES can work faster than humans.
Customers can have direct access to help desk ES – A self service help desk
2. Decreased decision marketing time
Using the system’s recommendation, a human can make much faster
decisions.
3. Increased process and product quality
ES can increase quality by providing consistent advice and reducing the size
and rate or errors.
4. Reduced downtime
By using ES it is possible to reduce machine downtime significantly.
5. Capture of scare expertise
The scarcity of expertise becomes evident in situation where there are not
enough experts for a task.
6. Flexibility
ES can offer flexibility in both service and manufacturing industries.
7. Easier equipment operation
ES makes complex equipment easier to operate.
8. Elimination of the need for expensive equipment
ES can perform the same task as human expert does with lower cost
instruments because of their ability to investigate the information provided by
instruments more thoroughly and quickly.
9. Operation in hazardous environment
Many tasks require human to operate in hazardous environments. The ES
may enable humans to avoid such environments. It enables workers to avoid
hot, humid, or toxic environment.
10. Accessibility to knowledge and help desks
ES make knowledge accessible, thus freeing experts from routine work
People can query systems and receive advice
11. Increased capabilities of other computerized systems
Integration of ES with other systems makes the other systems more effective.
12. Integration of several experts opinions
In certain cases, ES forces us to integration the opinions of several experts
and thus may increase the quality of the advice.
13. Ability to work with incomplete or uncertain information
ES can, like human experts, work with incomplete, imprecise, uncertain data,
information, or knowledge.
14. Provide training
Novices who work with ES become more and more experience.
15. Enhancement of problem solving and decision making
ES enhance problem solving by allowing the integration of top experts
judge met into analysis.
16. Improved decision making process
ES provide rapid feedback on decision consequences facilitate
communication among decision makers in a tea, and allow rapid response to
unforeseen changes in the environment.
17. Improved decision quality
ES are reliable, thy do not become tired or bored, call in sick or go on strike,
and thy do not talk back to the boss.
18. Ability to solve complex problems
One day. ES may solve problems whose complexity exceeds human ability.
19. Knowledge transfer to remote locations
One of the greatest potential benefits of ES is its ease of transfer across
international boundaries.
20. Enhancement of other CBIS
ES can often be fond providing intelligent capabilities to large CBIS. Many of
these benefits lead to improved decision making, improved products and
customer service, and a sustainable strategic advantage.
1. Increased output and productivity
ES can work faster than humans.
Customers can have direct access to help desk ES – A self service help desk
2. Decreased decision marketing time
Using the system’s recommendation, a human can make much faster
decisions.
3. Increased process and product quality
ES can increase quality by providing consistent advice and reducing the size
and rate or errors.
4. Reduced downtime
By using ES it is possible to reduce machine downtime significantly.
5. Capture of scare expertise
The scarcity of expertise becomes evident in situation where there are not
enough experts for a task.
6. Flexibility
ES can offer flexibility in both service and manufacturing industries.
7. Easier equipment operation
ES makes complex equipment easier to operate.
8. Elimination of the need for expensive equipment
ES can perform the same task as human expert does with lower cost
instruments because of their ability to investigate the information provided by
instruments more thoroughly and quickly.
9. Operation in hazardous environment
Many tasks require human to operate in hazardous environments. The ES
may enable humans to avoid such environments. It enables workers to avoid
hot, humid, or toxic environment.
10. Accessibility to knowledge and help desks
ES make knowledge accessible, thus freeing experts from routine work
People can query systems and receive advice
11. Increased capabilities of other computerized systems
Integration of ES with other systems makes the other systems more effective.
12. Integration of several experts opinions
In certain cases, ES forces us to integration the opinions of several experts
and thus may increase the quality of the advice.
13. Ability to work with incomplete or uncertain information
ES can, like human experts, work with incomplete, imprecise, uncertain data,
information, or knowledge.
14. Provide training
Novices who work with ES become more and more experience.
15. Enhancement of problem solving and decision making
ES enhance problem solving by allowing the integration of top experts
judge met into analysis.
16. Improved decision making process
ES provide rapid feedback on decision consequences facilitate
communication among decision makers in a tea, and allow rapid response to
unforeseen changes in the environment.
17. Improved decision quality
ES are reliable, thy do not become tired or bored, call in sick or go on strike,
and thy do not talk back to the boss.
18. Ability to solve complex problems
One day. ES may solve problems whose complexity exceeds human ability.
19. Knowledge transfer to remote locations
One of the greatest potential benefits of ES is its ease of transfer across
international boundaries.
20. Enhancement of other CBIS
ES can often be fond providing intelligent capabilities to large CBIS. Many of
these benefits lead to improved decision making, improved products and
customer service, and a sustainable strategic advantage.
The Human Element in Expert Systems
The Human Element in Expert Systems
At least two humans, and possibly more participate in the development
and use of an expert system.
The Expert
The expert is a person who has special knowledge, judgment,
experience, and methods along with the ability to apply these talents to
give advice and solve problems.
The knowledge engineer
The knowledge engineer helps the expert structure the problem area
by interpreting and integrating human answers to questions, drawing
analogies, posing counter examples, and bringing to light conceptual
difficulties.
The user
Most computer based systems have evolved into a single user mode.
In contrast, an ES has several possible classes of users.
o A non expert client seeking direct advice. In such case, the ES
act as a consultant or advisor.
o A student who wants to learn. The ES acts as an instructor.
o An ES builder who wants to improve or increase the knowledge
base. Here the ES acts as a partner.
How Expert system work
ES construction and use consists of three major activities.
1. Development
2. Consultation
3. Improvement
Development
The development of an expert system involves the construction of the
knowledge base by acquiring knowledge from experts or documented
sources.
Development activity also includes the construction (or acquisition) of
an inference engine, a blackboard, an explanation facility, and any
other required software, such as interfaces.
The major participants in this activity are the domain expert, the
knowledge engineer, and possibly information system analysts and
programmers.
A tool that is often used to expedite development is called the ES shell.
ES shells include all the generic components of an ES, but they do not
include the knowledge.
Consultation
Once the system is developed and validated, it is deployed to the
users, who are typically novices.
When users seed advices, they access the ES.
Improvement
ES are improved several times through a process called rapid
prototyping during their development.
Problem Areas addressed by Expert System
Prediction
Systems
This includes weather forecasting, demographic predictions, economic
forecasting, traffic predictions, crop estimates, and military, marketing, or
financial forecasting.
Diagnostic This system typically relates observed behavioral irregularities to underlying
Systems causes.
This includes medical, electronic, mechanical, & software diagnosis.
Design
Systems
This develops configurations of objects that satisfy the constraints of the
design problem.
Such problems include circuit layout, building design, and plan layout.
Planning
Systems
This specializes in problems of planning, such as automatic programming.
They also deal with short and long term planning areas such as project
management, routing, communications, product development, military
applications, and financial planning.
Monitoring
Systems
This compares observations of system behavior with standards than seem
crucial for successful goal attainment.
Air traffic control to fiscal management tasks
Debugging
Systems
This relies on planning, design, and prediction capabilities to create
specifications or recommendations for correcting a diagnosed problem.
Repair
Systems
This develops and executes plans to administer a remedy for certain
diagnosed problems.
Such systems incorporate debugging, planning and execution capabilities.
Instruction
Systems
This incorporates diagnosis and debugging subsystems that specifically
address the student needs.
Control
Systems
These adaptively govern the overall behavior of a system.
At least two humans, and possibly more participate in the development
and use of an expert system.
The Expert
The expert is a person who has special knowledge, judgment,
experience, and methods along with the ability to apply these talents to
give advice and solve problems.
The knowledge engineer
The knowledge engineer helps the expert structure the problem area
by interpreting and integrating human answers to questions, drawing
analogies, posing counter examples, and bringing to light conceptual
difficulties.
The user
Most computer based systems have evolved into a single user mode.
In contrast, an ES has several possible classes of users.
o A non expert client seeking direct advice. In such case, the ES
act as a consultant or advisor.
o A student who wants to learn. The ES acts as an instructor.
o An ES builder who wants to improve or increase the knowledge
base. Here the ES acts as a partner.
How Expert system work
ES construction and use consists of three major activities.
1. Development
2. Consultation
3. Improvement
Development
The development of an expert system involves the construction of the
knowledge base by acquiring knowledge from experts or documented
sources.
Development activity also includes the construction (or acquisition) of
an inference engine, a blackboard, an explanation facility, and any
other required software, such as interfaces.
The major participants in this activity are the domain expert, the
knowledge engineer, and possibly information system analysts and
programmers.
A tool that is often used to expedite development is called the ES shell.
ES shells include all the generic components of an ES, but they do not
include the knowledge.
Consultation
Once the system is developed and validated, it is deployed to the
users, who are typically novices.
When users seed advices, they access the ES.
Improvement
ES are improved several times through a process called rapid
prototyping during their development.
Problem Areas addressed by Expert System
Prediction
Systems
This includes weather forecasting, demographic predictions, economic
forecasting, traffic predictions, crop estimates, and military, marketing, or
financial forecasting.
Diagnostic This system typically relates observed behavioral irregularities to underlying
Systems causes.
This includes medical, electronic, mechanical, & software diagnosis.
Design
Systems
This develops configurations of objects that satisfy the constraints of the
design problem.
Such problems include circuit layout, building design, and plan layout.
Planning
Systems
This specializes in problems of planning, such as automatic programming.
They also deal with short and long term planning areas such as project
management, routing, communications, product development, military
applications, and financial planning.
Monitoring
Systems
This compares observations of system behavior with standards than seem
crucial for successful goal attainment.
Air traffic control to fiscal management tasks
Debugging
Systems
This relies on planning, design, and prediction capabilities to create
specifications or recommendations for correcting a diagnosed problem.
Repair
Systems
This develops and executes plans to administer a remedy for certain
diagnosed problems.
Such systems incorporate debugging, planning and execution capabilities.
Instruction
Systems
This incorporates diagnosis and debugging subsystems that specifically
address the student needs.
Control
Systems
These adaptively govern the overall behavior of a system.
Knowledge refining system
Knowledge refining system
Knowledge acquisition subsystem
Knowledge acquisition is the accumulation, transfer and transformation
of problem solving expertise from experts or documented knowledge
sources to a computer program for construction or expanding the
knowledge base.
Potential sources of knowledge include human experts, text books,
multimedia documents, databases, special research reports, and
information available over the World Wide Web.
Typically the knowledge engineer helps the expert structure the
problem area by interpreting and integrating human answers to
questions, drawing analogies, posing counter examples, and bringing
to light conceptual difficulties.
Knowledge base
The knowledge base contains the knowledge necessary for
understanding, formulating, and solving problems.
It includes two basic elements.
1. Facts such as the problem situation and theory of the problem
area
2. Special heuristics or rules that direct the use of knowledge t
solve specific problems in a particular domain.
The heuristics express the informal judgmental knowledge in an
application area.
Inference Engine
The brain of the ES is the inference engine, also known as the control
structure or rule interpreter.
This component is essentially a computer program that provides
methodology for reasoning about information in the knowledge base
and on the blackboard, and for formulating conclusions.
The inference engine has three major elements.
1) An interpreter This executes the chosen agenda items by applying
the corresponding knowledge base rules.
2) A scheduler This maintains control over the agenda.
It estimates the effects of applying inference rules in
light of item priorities or other criteria on the agenda.
3) A consistency enforcer This attempts to maintain a consistent representation
of the emerging solution.
User interface
Expert systems contain a language processor for friendly, problem
oriented communication between the user and the computer.
This communication can best be carried out in a natural language.
Sometimes it is supplemented by menus and graphics.
Blackboard - workplace
The blackboard is an area of working memory set aside for the
description of a current problem, as specified by the input data. It is
also used for recording intermediate results.
The blackboard records intermediate hypotheses and decisions.
Three types of decisions can be recorded on the blackboard.
1. A plan - how to attack the problem
2. An agenda – potential actions awaiting execution
3. A Solution – candidate hypotheses and alternative courses of
action that the system has generated
Explanation subsystem - Justifier
The ability to trace responsibility for conclusions to their sources is
crucial both in the transfer of expertise and in problem solving.
The explanation subsystem can trace such responsibility and explain
the ES behavior by interactively answering questions.
Knowledge refining system
Human experts have a knowledge refining system, that is, they can
analyze their own knowledge, and its use, learn from it, and improve on
tit for future consolations.
Similarly, such evaluation is necessary in computerized learning, so
that the program can analyze the reason for its success or failure.
Such a component is not available in commercial expert system at the
moment, but is being developed in experimental ES.
Knowledge acquisition subsystem
Knowledge acquisition is the accumulation, transfer and transformation
of problem solving expertise from experts or documented knowledge
sources to a computer program for construction or expanding the
knowledge base.
Potential sources of knowledge include human experts, text books,
multimedia documents, databases, special research reports, and
information available over the World Wide Web.
Typically the knowledge engineer helps the expert structure the
problem area by interpreting and integrating human answers to
questions, drawing analogies, posing counter examples, and bringing
to light conceptual difficulties.
Knowledge base
The knowledge base contains the knowledge necessary for
understanding, formulating, and solving problems.
It includes two basic elements.
1. Facts such as the problem situation and theory of the problem
area
2. Special heuristics or rules that direct the use of knowledge t
solve specific problems in a particular domain.
The heuristics express the informal judgmental knowledge in an
application area.
Inference Engine
The brain of the ES is the inference engine, also known as the control
structure or rule interpreter.
This component is essentially a computer program that provides
methodology for reasoning about information in the knowledge base
and on the blackboard, and for formulating conclusions.
The inference engine has three major elements.
1) An interpreter This executes the chosen agenda items by applying
the corresponding knowledge base rules.
2) A scheduler This maintains control over the agenda.
It estimates the effects of applying inference rules in
light of item priorities or other criteria on the agenda.
3) A consistency enforcer This attempts to maintain a consistent representation
of the emerging solution.
User interface
Expert systems contain a language processor for friendly, problem
oriented communication between the user and the computer.
This communication can best be carried out in a natural language.
Sometimes it is supplemented by menus and graphics.
Blackboard - workplace
The blackboard is an area of working memory set aside for the
description of a current problem, as specified by the input data. It is
also used for recording intermediate results.
The blackboard records intermediate hypotheses and decisions.
Three types of decisions can be recorded on the blackboard.
1. A plan - how to attack the problem
2. An agenda – potential actions awaiting execution
3. A Solution – candidate hypotheses and alternative courses of
action that the system has generated
Explanation subsystem - Justifier
The ability to trace responsibility for conclusions to their sources is
crucial both in the transfer of expertise and in problem solving.
The explanation subsystem can trace such responsibility and explain
the ES behavior by interactively answering questions.
Knowledge refining system
Human experts have a knowledge refining system, that is, they can
analyze their own knowledge, and its use, learn from it, and improve on
tit for future consolations.
Similarly, such evaluation is necessary in computerized learning, so
that the program can analyze the reason for its success or failure.
Such a component is not available in commercial expert system at the
moment, but is being developed in experimental ES.
Accessing the GroupSystems tools
Accessing the GroupSystems tools
The tools are accessed from the Agenda (Which is the control panel),
The agenda offers a rich collection of features to support the session
leader and the group as they work together to accomplish objectives
during a face to face meeting.
The group resources interact with other members of the group.
People People contain the list of participants with background information
for the meeting.
Whiteboard This is a group enabled drawing and annotation tool.
Handouts These are reference materials for group viewing such as a report,
agenda, or spreadsheet file.
Opinion Meter This is a faster, simpler version of the vote tool.
It allows us to quickly determine the group’s opinions.
Briefcase This allows us to access your commonly used applications such as
word processing, calculators, and e mail.
Personal Log This allows us to make notes during a meeting.
Only we can access these notes.
Event Monitor This keeps s informed of new activities and new information in
existing activities.
Expert System [EI]
An expert system is a system that uses human knowledge captured in
a computer to solve problems that ordinarily require human expertise.
General Purpose Problem solver (GPS)
The general purpose problem solver, a procedure developed by Newell
and Simon from their logic theory machine.
GPS attempts to find operations that reduce the difference between a
goal and current states.
Basic Concept of Expert
The basic concepts of expert system revolve around expertise; expert’s
transferring expertly inferencing rules, and explanation capability.
Expertise is the extensive, task specific knowledge acquired from
training, reading and experience.
The following types of knowledge are examples of what expertise
include…
Theories about the problem area.
Rules and procedures regarding the general problem area.
Rules (heuristics) of what to do in a given problem situation.
Global strategies for solving these types of problems.
Meta knowledge – knowledge about knowledge
Fact about the problem area.
Expert
Typically, human expertise includes a constellation of behavior that
involves the following activities.
Recognizing and formulating the problem
Solving the problem quickly and properly
Explain the solution
Learning from solution
Restructuring knowledge
Breaking rules
Determining relevance
Degrading gracefully – awareness of limitation
o Experts would be able to explain the results, learn new thing about the
domain, restructure knowledge whenever needed, break rules
whenever necessary, and determine whether their expertise is
relevant.
o Experts degrade gracefully. "Meaning that as they approach the
boundaries of their knowledge, they gradually become less proficient at
solving problems, but can still develop reasonable solutions".
Transferring Expertise
The objective of an expert system is to transfer expertise from an
expert to a computer system and then on to other humans (non expert)
These processes involve four activities.
1. Knowledge acquisition – from experts or other recourses
2. Knowledge representation – in the computer
3. Knowledge inferencing
4. Knowledge transfer – to the user.
The knowledge is entered in the computer in a component called
knowledge base.
The two types of knowledge are distinguished.
1. Facts 2. Procedures – usually rules
Inferencing
A unique feature of expert system is its ability to reason (Think).
The inferencing is performed in a component called the inference
engine that includes procedures regarding problem solving.
Rules
Most commercial ES are Rule based system.
The knowledge is assorted mainly in the form of rules, as are the
problem solving procedures.
Structure of Expert Systems
ES are composed of two major parts.
1. The development environment
2. Te consultation (runtime) environment
The ES builder o builds the components and put knowledge into the
knowledge base uses the development environment.
The three major components that appear virtually in every expert
system are …
1. The knowledge base
2. Inference engine
3. User interface
In general, though an expert system may contain the following
components…
1) Knowledge acquisition subsystem 5) User interface
2) Knowledge base 6) Blackboard – workplace
3) Inference engine 7) Explanation subsystem – Justifier
4) User
The tools are accessed from the Agenda (Which is the control panel),
The agenda offers a rich collection of features to support the session
leader and the group as they work together to accomplish objectives
during a face to face meeting.
The group resources interact with other members of the group.
People People contain the list of participants with background information
for the meeting.
Whiteboard This is a group enabled drawing and annotation tool.
Handouts These are reference materials for group viewing such as a report,
agenda, or spreadsheet file.
Opinion Meter This is a faster, simpler version of the vote tool.
It allows us to quickly determine the group’s opinions.
Briefcase This allows us to access your commonly used applications such as
word processing, calculators, and e mail.
Personal Log This allows us to make notes during a meeting.
Only we can access these notes.
Event Monitor This keeps s informed of new activities and new information in
existing activities.
Expert System [EI]
An expert system is a system that uses human knowledge captured in
a computer to solve problems that ordinarily require human expertise.
General Purpose Problem solver (GPS)
The general purpose problem solver, a procedure developed by Newell
and Simon from their logic theory machine.
GPS attempts to find operations that reduce the difference between a
goal and current states.
Basic Concept of Expert
The basic concepts of expert system revolve around expertise; expert’s
transferring expertly inferencing rules, and explanation capability.
Expertise is the extensive, task specific knowledge acquired from
training, reading and experience.
The following types of knowledge are examples of what expertise
include…
Theories about the problem area.
Rules and procedures regarding the general problem area.
Rules (heuristics) of what to do in a given problem situation.
Global strategies for solving these types of problems.
Meta knowledge – knowledge about knowledge
Fact about the problem area.
Expert
Typically, human expertise includes a constellation of behavior that
involves the following activities.
Recognizing and formulating the problem
Solving the problem quickly and properly
Explain the solution
Learning from solution
Restructuring knowledge
Breaking rules
Determining relevance
Degrading gracefully – awareness of limitation
o Experts would be able to explain the results, learn new thing about the
domain, restructure knowledge whenever needed, break rules
whenever necessary, and determine whether their expertise is
relevant.
o Experts degrade gracefully. "Meaning that as they approach the
boundaries of their knowledge, they gradually become less proficient at
solving problems, but can still develop reasonable solutions".
Transferring Expertise
The objective of an expert system is to transfer expertise from an
expert to a computer system and then on to other humans (non expert)
These processes involve four activities.
1. Knowledge acquisition – from experts or other recourses
2. Knowledge representation – in the computer
3. Knowledge inferencing
4. Knowledge transfer – to the user.
The knowledge is entered in the computer in a component called
knowledge base.
The two types of knowledge are distinguished.
1. Facts 2. Procedures – usually rules
Inferencing
A unique feature of expert system is its ability to reason (Think).
The inferencing is performed in a component called the inference
engine that includes procedures regarding problem solving.
Rules
Most commercial ES are Rule based system.
The knowledge is assorted mainly in the form of rules, as are the
problem solving procedures.
Structure of Expert Systems
ES are composed of two major parts.
1. The development environment
2. Te consultation (runtime) environment
The ES builder o builds the components and put knowledge into the
knowledge base uses the development environment.
The three major components that appear virtually in every expert
system are …
1. The knowledge base
2. Inference engine
3. User interface
In general, though an expert system may contain the following
components…
1) Knowledge acquisition subsystem 5) User interface
2) Knowledge base 6) Blackboard – workplace
3) Inference engine 7) Explanation subsystem – Justifier
4) User
The Technology of GDSS
The Technology of GDSS
There are three technology options for providing GDSS.
1. A special purpose electronic meeting facility
2. Custom designed for GDSS, a general purpose computer lab
that doubles as a less elegant but equally useful GDSS facility
3. Web (Internet), intranet, or LAN based software that allows
groups to work from any location at any time.
DeSanctis and Gallupe define the components of GDSS as hardware,
software, people and procedures.
Hardware
Single PC Participants gather around a single PC while one person
accesses and enters data and models.
PCs and Key
Board
This is basically a collection of workstations, each equipped
with keypads for voting.
A Decision Room This is a GDSS facility dedicated to and designed for
electronic meeting.
Distributed GDSS The participants are in different locations using PCs,
networks, and standard operating systems.
Software
GDSS software includes modules for support the individual, the group,
the process, and specific tasks.
Example _ the software may include special routines for improving the
decision making process and an easy to use, flexible user interface.
Typical group features include…
Numerical and graphical summarization of group members, ideas, and
votes.
Programs for the calculation of weights for decision alternatives,
anonymous recording of ideas, formal selection of a group leader,
progressive rounds of voting toward consensus building or elimination
of redundant input during brainstorming.
Text and data transmission among the group members, between the
group members and the facilitator, and between the members and a
central data / document repository.
People
The people component of the GDSS includes the group members and
a facilitator, who serves as the group’s chauffeur, operating the GDSS
hardware and software, and displaying requested information to the
group as needed.
Procedures
The final component of the GDSS consists of the procedures that
enable ease of operation and effective use of the technology by group
members.
GDSS Software Packages
The following are the major comprehensive GDSS software packages
that are primarily used in a decision room environment.
GroupSystems for
Windows
This is a comprehensive set of electronic meeting software.
VisionQuest This product supports a wide range of interactive team functions
such as control over the agenda, prioritizing, idea generation,
and the documentation of activities.
It also supports meetings in which the participants are in
different locations and can even communicate at different times.
SAMM This software from the University of Minnesota has been
installed in several corporate decision rooms
Lotus
Domino/Notes
This software is applicable to executive information system
deployment.
Netscape
Communicator
This software provides many of the features of Lotus notes
directly via Web servers and browsers.
TCBWorks This is one of the emerging Web based GDSS packages. It runs
on Unix servers.
The GROUPSYSTEMS for Windows standard tools support group
processes such as brainstorming, list building, information gathering,
voting, organizing, prioritizing, and consensus building.
The standard tools are as follows.
Electronic
brainstorming
This is designed to gather ideas and comments in an
unstructured manner.
Group Outliner This allows the group to create and comment on a multilevel list
of topics in a tree or outline structure.
Topic Commenter This allows participants to comment on a list of topics. It
supports idea generation in a more structured format than
Electronic Brainstorming, but less structured than Group
Outliner.
Categoriser This allows the group to generate a list of ideas and supporting
comments. Categories are created for the ideas, and
participants can drag the ideas into the desired category.
Vote This supports consensus development through group evaluation
of issues.
Voting methods include the following quantifiable ways.
Rank order, Multiple selection, 4 point and 5 point
agree/disagree, 10 point scale, yes/no, true/false, and a user
defined custom method.
The GROUPSYSTEMS for Windows advanced tools include add-ins
for analysis, surveys, and modeling.
The advance tools are as follows.
Alternative analysis This allows the group to weight or rate a list of alternatives
against a list of criteria because collaborative decisions require
the evaluation of multiple perspectives and ideas.
Survey This allows the creation, administration and analysis of an online
questionnaire.
This may be distributed to participant workstation in a wide
variety of setting from a group meeting to the World Wide Web.
Activity Modeler This provides user friendly group support for simultaneous
business process reengineering modeling.
Using activity modeler, people from all levels in an organization
can participate in the modeling process, producing more
accurate and productive business models, quickly.
There are three technology options for providing GDSS.
1. A special purpose electronic meeting facility
2. Custom designed for GDSS, a general purpose computer lab
that doubles as a less elegant but equally useful GDSS facility
3. Web (Internet), intranet, or LAN based software that allows
groups to work from any location at any time.
DeSanctis and Gallupe define the components of GDSS as hardware,
software, people and procedures.
Hardware
Single PC Participants gather around a single PC while one person
accesses and enters data and models.
PCs and Key
Board
This is basically a collection of workstations, each equipped
with keypads for voting.
A Decision Room This is a GDSS facility dedicated to and designed for
electronic meeting.
Distributed GDSS The participants are in different locations using PCs,
networks, and standard operating systems.
Software
GDSS software includes modules for support the individual, the group,
the process, and specific tasks.
Example _ the software may include special routines for improving the
decision making process and an easy to use, flexible user interface.
Typical group features include…
Numerical and graphical summarization of group members, ideas, and
votes.
Programs for the calculation of weights for decision alternatives,
anonymous recording of ideas, formal selection of a group leader,
progressive rounds of voting toward consensus building or elimination
of redundant input during brainstorming.
Text and data transmission among the group members, between the
group members and the facilitator, and between the members and a
central data / document repository.
People
The people component of the GDSS includes the group members and
a facilitator, who serves as the group’s chauffeur, operating the GDSS
hardware and software, and displaying requested information to the
group as needed.
Procedures
The final component of the GDSS consists of the procedures that
enable ease of operation and effective use of the technology by group
members.
GDSS Software Packages
The following are the major comprehensive GDSS software packages
that are primarily used in a decision room environment.
GroupSystems for
Windows
This is a comprehensive set of electronic meeting software.
VisionQuest This product supports a wide range of interactive team functions
such as control over the agenda, prioritizing, idea generation,
and the documentation of activities.
It also supports meetings in which the participants are in
different locations and can even communicate at different times.
SAMM This software from the University of Minnesota has been
installed in several corporate decision rooms
Lotus
Domino/Notes
This software is applicable to executive information system
deployment.
Netscape
Communicator
This software provides many of the features of Lotus notes
directly via Web servers and browsers.
TCBWorks This is one of the emerging Web based GDSS packages. It runs
on Unix servers.
The GROUPSYSTEMS for Windows standard tools support group
processes such as brainstorming, list building, information gathering,
voting, organizing, prioritizing, and consensus building.
The standard tools are as follows.
Electronic
brainstorming
This is designed to gather ideas and comments in an
unstructured manner.
Group Outliner This allows the group to create and comment on a multilevel list
of topics in a tree or outline structure.
Topic Commenter This allows participants to comment on a list of topics. It
supports idea generation in a more structured format than
Electronic Brainstorming, but less structured than Group
Outliner.
Categoriser This allows the group to generate a list of ideas and supporting
comments. Categories are created for the ideas, and
participants can drag the ideas into the desired category.
Vote This supports consensus development through group evaluation
of issues.
Voting methods include the following quantifiable ways.
Rank order, Multiple selection, 4 point and 5 point
agree/disagree, 10 point scale, yes/no, true/false, and a user
defined custom method.
The GROUPSYSTEMS for Windows advanced tools include add-ins
for analysis, surveys, and modeling.
The advance tools are as follows.
Alternative analysis This allows the group to weight or rate a list of alternatives
against a list of criteria because collaborative decisions require
the evaluation of multiple perspectives and ideas.
Survey This allows the creation, administration and analysis of an online
questionnaire.
This may be distributed to participant workstation in a wide
variety of setting from a group meeting to the World Wide Web.
Activity Modeler This provides user friendly group support for simultaneous
business process reengineering modeling.
Using activity modeler, people from all levels in an organization
can participate in the modeling process, producing more
accurate and productive business models, quickly.
Decision Making in Groups
Decision Making in Groups
Groups: The term group refers to two or more (Usually up to 25)
people whose mission is to perform some task and who act as one
unit. The group can be permanent or temporary.
The group can be in one location or in several locations, and it can
meet concurrently or its members can work at different times.
A group can be a committee, a review panel, a task force, an executive
board, or a permanent team
Decision making is usually a shared process.
These groups meeting are characterized by the following activities and
processes.
o Meetings are a joint activity, engaged in by a group of people,
usually of equal or near equal status
o The outcome of the meeting depends partly on the knowledge,
opinions, and judgments of it participants
o The outcome of the meeting also depends on the composition
of the groups and on the decision making process used by the
group
o Differences in opinion are settled either by the ranking person
present or, more often, by negotiation or arbitration.
Despite the many benefits of group interaction, the results are not
always successful. The reason is that the process of collaborative work
is often plagued with dysfunctions, called process losses.
Dispersed Groups: GDSS can support meetings in which members
are in different locations and even at different times. New groupware
has been developed where corporate intranets or the internet.
The nominal group technique (NGT)
The NGT is one of the earliest managerial methods specifically
designed to support group work.
The method, developed by Delbec and Van de includes a sequence of
activities in decision making process.
1. Silent generation of ideas in writing
2. Round robin listing of ideas on a flip chart
3. Serial discussion of ideas
4. Silent listing and ranking of priorities
5. Discussion of priorities
6. Silent re ranking and search, which indicates that this procedure
is superior to conventional discussion groups in terms of
generating higher duality, greater quality and improved
distribution of information on fact finding tasks?
Group Decision Support Systems (GDSS)
There were two GDSS schools of thought.
The social sciences approach, which built on sociological and cognitive
theories of people working in the groups, to determine which types of
tools, would be most effective.
The Engineering approach, which examined how people interacted at
meetings and derived tools to improve the groups’ effectiveness and
efficiency.
Over the time both schools recognized the positive aspects of each
viewpoint, leading to an effective merger of the two.
What is GDSS?
According to Huber a GDSS consists of a set of software, hardware,
language components and procedures that support a group of people
engaged in a decision related meeting.
Following are the characteristics of GDSS.
o The GDSS is a specially designed information system.
o A GDSS is designed with the goal of supporting groups of
decision makes in their work
o A GDSS is easy to learn and to use.
o The GDSS may be designed for one type of problem or for a
variety of group level organizational decisions.
o The GDSS is designed to encourage activities such as idea
generation, conflict resolution, and freedom of expression.
o The GDSS contains built in mechanisms that discourage
development of negative group behaviors such as destructive
conflict, miscommunication, and group thing.
o GDSS is considered a subset of the broader field of Group
support system (GSS) or Electronic meeting systems (EMS).
EMS is nothing but an Information Technology (IT) based
environment that supports group meetings, which may be
distributed geographically and temporally.
Goal of GDSS and its Technology Levels
DeSanctis and Gallupe divide GDSS technologies into three levels that
differ in terms of the support provided to a group decision maker.
Level 1: Process support
Level 2: Decision making support
Level 3: Rules of order
Level 1: Process support
The goal of Level 1 GDSS is to reduce or remove communication
barriers. Items that are supported by such a system are…
Electronic messaging among group members
Network linking each member’s personal computer to those of the
other group members, the facilitator, the public screen, databases, or
any other common CBIS
A Public screen available at each group member’s PC or visible to all
members in a central place
Anonymous input of ideas and votes to enhance participation of group
members who prefer anonymity
Active solicitation of ideas or votes from each group member to
encourage participation and induce creativity
Summery and display of ideas and opinions, including statistical
summaries and vote displays
A format for an agenda that can be agreed upon by the group, to aid
meeting organization
Continuous display of the agenda, as well as other information, to keep
meetings of schedule
Level 2: Decision making support
At this level, the software adds capabilities for modeling and decision
analysis, process by providing systematic methods for task gains.
Features in this category include…
Planning and financial models
Decision trees
Probability assessment models
Resource allocation models
Social judgment models
Level 3: Rules of order
Level 3 addresses the group’s decision making process in terms of
controlling its timing, content, or message patterns.
At this level, special software containing rules of order is added.
Example _ some rules could determine the sequence of speaking, the
appropriate response, or voting rules.
Groups: The term group refers to two or more (Usually up to 25)
people whose mission is to perform some task and who act as one
unit. The group can be permanent or temporary.
The group can be in one location or in several locations, and it can
meet concurrently or its members can work at different times.
A group can be a committee, a review panel, a task force, an executive
board, or a permanent team
Decision making is usually a shared process.
These groups meeting are characterized by the following activities and
processes.
o Meetings are a joint activity, engaged in by a group of people,
usually of equal or near equal status
o The outcome of the meeting depends partly on the knowledge,
opinions, and judgments of it participants
o The outcome of the meeting also depends on the composition
of the groups and on the decision making process used by the
group
o Differences in opinion are settled either by the ranking person
present or, more often, by negotiation or arbitration.
Despite the many benefits of group interaction, the results are not
always successful. The reason is that the process of collaborative work
is often plagued with dysfunctions, called process losses.
Dispersed Groups: GDSS can support meetings in which members
are in different locations and even at different times. New groupware
has been developed where corporate intranets or the internet.
The nominal group technique (NGT)
The NGT is one of the earliest managerial methods specifically
designed to support group work.
The method, developed by Delbec and Van de includes a sequence of
activities in decision making process.
1. Silent generation of ideas in writing
2. Round robin listing of ideas on a flip chart
3. Serial discussion of ideas
4. Silent listing and ranking of priorities
5. Discussion of priorities
6. Silent re ranking and search, which indicates that this procedure
is superior to conventional discussion groups in terms of
generating higher duality, greater quality and improved
distribution of information on fact finding tasks?
Group Decision Support Systems (GDSS)
There were two GDSS schools of thought.
The social sciences approach, which built on sociological and cognitive
theories of people working in the groups, to determine which types of
tools, would be most effective.
The Engineering approach, which examined how people interacted at
meetings and derived tools to improve the groups’ effectiveness and
efficiency.
Over the time both schools recognized the positive aspects of each
viewpoint, leading to an effective merger of the two.
What is GDSS?
According to Huber a GDSS consists of a set of software, hardware,
language components and procedures that support a group of people
engaged in a decision related meeting.
Following are the characteristics of GDSS.
o The GDSS is a specially designed information system.
o A GDSS is designed with the goal of supporting groups of
decision makes in their work
o A GDSS is easy to learn and to use.
o The GDSS may be designed for one type of problem or for a
variety of group level organizational decisions.
o The GDSS is designed to encourage activities such as idea
generation, conflict resolution, and freedom of expression.
o The GDSS contains built in mechanisms that discourage
development of negative group behaviors such as destructive
conflict, miscommunication, and group thing.
o GDSS is considered a subset of the broader field of Group
support system (GSS) or Electronic meeting systems (EMS).
EMS is nothing but an Information Technology (IT) based
environment that supports group meetings, which may be
distributed geographically and temporally.
Goal of GDSS and its Technology Levels
DeSanctis and Gallupe divide GDSS technologies into three levels that
differ in terms of the support provided to a group decision maker.
Level 1: Process support
Level 2: Decision making support
Level 3: Rules of order
Level 1: Process support
The goal of Level 1 GDSS is to reduce or remove communication
barriers. Items that are supported by such a system are…
Electronic messaging among group members
Network linking each member’s personal computer to those of the
other group members, the facilitator, the public screen, databases, or
any other common CBIS
A Public screen available at each group member’s PC or visible to all
members in a central place
Anonymous input of ideas and votes to enhance participation of group
members who prefer anonymity
Active solicitation of ideas or votes from each group member to
encourage participation and induce creativity
Summery and display of ideas and opinions, including statistical
summaries and vote displays
A format for an agenda that can be agreed upon by the group, to aid
meeting organization
Continuous display of the agenda, as well as other information, to keep
meetings of schedule
Level 2: Decision making support
At this level, the software adds capabilities for modeling and decision
analysis, process by providing systematic methods for task gains.
Features in this category include…
Planning and financial models
Decision trees
Probability assessment models
Resource allocation models
Social judgment models
Level 3: Rules of order
Level 3 addresses the group’s decision making process in terms of
controlling its timing, content, or message patterns.
At this level, special software containing rules of order is added.
Example _ some rules could determine the sequence of speaking, the
appropriate response, or voting rules.
The development Process: Life Cycle versus Prototyping
The development Process: Life Cycle versus Prototyping
DSS construction can be differentiating between two extremes.
1. The life cycle approach
2. The evolutionary prototyping approach (iterative process)
The system development life cycle (SDLC) approach and DSS
This approach is used in transaction processing systems, which are
fairly structured.
The evolutionary prototyping approach
The prototyping approach aims at building a DSS in a series of short
steps with immediate feedback from users to ensure that development
is preceding correctly therefore, DSS tools must permit changes to be
made quickly and easily.
Advantages of prototyping
Short development time
Short user reaction time – feedback from user
Improved user understanding of the system, its information needs, and
its capabilities
Low cost
Disadvantages and limitations
Selection of DSS Development Tools
Mainframe DSS software The selection of a DSS generator
PC DSS software Developing DSS
Software selection
Mainframes DSS Software
Mainframe DSS software costs between $30,000 and $300,000 and
has several powerful capabilities.
Example _ Metafact (From integrated data architects, Northridge, (CA)
costs over $200,000.
PC DSS Software
Several vendors offer PC versions of their mainframe products at a
considerably low price.
Software selection
At the time of the selection, DSS information requirements and outputs
are not completely known.
There are hundreds of software packages on the market.
The software packages are changing very rapidly.
Price changes are frequent.
Several people may be involved in the evaluation team
Technical, functional, end user and managerial issues are all
considered.
Developing DSS
Development tools increase the productivity of builders and help them
produce, at a moderate cost, a DSS responsive to the true needs of
users.
A DSS development system can be thought of as s shop with several
tools and components and these components are integrated into a
DSS generator.
DSS construction can be differentiating between two extremes.
1. The life cycle approach
2. The evolutionary prototyping approach (iterative process)
The system development life cycle (SDLC) approach and DSS
This approach is used in transaction processing systems, which are
fairly structured.
The evolutionary prototyping approach
The prototyping approach aims at building a DSS in a series of short
steps with immediate feedback from users to ensure that development
is preceding correctly therefore, DSS tools must permit changes to be
made quickly and easily.
Advantages of prototyping
Short development time
Short user reaction time – feedback from user
Improved user understanding of the system, its information needs, and
its capabilities
Low cost
Disadvantages and limitations
Selection of DSS Development Tools
Mainframe DSS software The selection of a DSS generator
PC DSS software Developing DSS
Software selection
Mainframes DSS Software
Mainframe DSS software costs between $30,000 and $300,000 and
has several powerful capabilities.
Example _ Metafact (From integrated data architects, Northridge, (CA)
costs over $200,000.
PC DSS Software
Several vendors offer PC versions of their mainframe products at a
considerably low price.
Software selection
At the time of the selection, DSS information requirements and outputs
are not completely known.
There are hundreds of software packages on the market.
The software packages are changing very rapidly.
Price changes are frequent.
Several people may be involved in the evaluation team
Technical, functional, end user and managerial issues are all
considered.
Developing DSS
Development tools increase the productivity of builders and help them
produce, at a moderate cost, a DSS responsive to the true needs of
users.
A DSS development system can be thought of as s shop with several
tools and components and these components are integrated into a
DSS generator.
How speech recognition system work
How speech recognition system work
The voice input to the microphone produces an analog speech signal.
This speech signal is then converted into binary words compatible with
a digital computer by an analog to digital converter.
The binary version of the input voice is then stored in the system and
compared to previously store binary representations of words.
When a match occurs, recognition is achieved.
Then, the spoken work appears on a video screen or is passed along
to an NLP for meaning analysis.
A typical work recognizer
The voice input is applied to a microphone.
The electrical analog signal from the microphone is fed to an amplifier,
where it is increased in level.
The amplifier contains some kind of Automatic gain control to provide
an output signal in a specific voltage range.
The analog signal representing a spoken work is a complex waveform
that contains many individual frequencies.
The way to recognize the spoken work is to break that complex input
signal into its components parts.
This is usually done with a set of filters.
A filter is an electronic circuit that passes or rejects frequencies in a
certain range.
Voice synthesis
Voice synthesis or response is the technology by which computers
speak.
Tools and applications of voice technologies
Speech recognition is used in four major areas.
1. Command and control – including voice control of machine
operations and voice activated dialing
2. Data entry to forms – quality control system, or databases
3. Direction – including creation of documents and reports
4. Data access / Information retrieval – such as directory
assistance and retrieval of online data
Voice technology application sampler – Application
Research on User Interfaces in
Hwang and Wu identified the following indecent and dependent
variables.
Independent Variables
Human user Demographics – Age, education, experience
Psychological – Cognitive style, intelligence, risk attitude
Decision
environment
Decision structure, organizational level
Others – stability, time pressure, uncertainty
Task Decision support – complexity level
Inquiry / information retrieval , data entry, word processing and
Computer aided instruction
Interface
characteristics
Input / Output media, Dialog type, Presentation format and
Language characteristics
Dependent variable : Human / Computer effectiveness
This was measured by usefulness, perceived ease of use and
performance (time, error, task, completion, profit), user attributes
(satisfaction, confidence) and use of system option (high. low).
Development Strategies
Write a customized DSS in general purpose programming language
such as COBOL or PASCAL.
Use a fourth generation language (4GL). Such as data oriented
languages, spreadsheets, and financial oriented languages.
Use a DSS integrated development tool. An integrated package
eliminates the need to use multiple 4GLs. The best known examples
for the PC are Excel, Lotus 1-2-3, and Quattro Pro.
Use a domain specific DSS generator: Domain specific DSS
generators are designed to build a highly structured system, usually in
a functional area.
DSS Development Phase A : Planning Phase E : Construction
Phase B : Research Phase F : Implementation
Phase C : System analysis and
conceptual design
Phase G : Maintenance and
documentation
Phase D : Design Phase H : Adaptation
Phase A : Planning
Planning deals mainly with need assessment and problem diagnosis.
A crucial step in the planning effort is determining the key decisions to
be supported by the DSS.
Example _ in a portfolio selection system, a key decision might be
selecting the correct stocks for a particular customer’s needs.
Phase B : Research
This phase involves the identification of a relevant approach for
addressing user needs and available resources. (Hardware, software,
vendors, systems, studies or related experience in other organizations,
and review of relevant research).
Phase C : System analysis and conceptual design
This phase includes the determination of the best construction
approach and specific resources required implementing it, including
technical, staff, financial, and organizational resources.
Phase D : Design
The detailed specifications of the system components, structure and
features are determined.
Phase E : Construction
A DSS can be constructed in different ways depending on the design
philosophy and the tools being used.
The construction is the technical implementation of the design.
Phase F : Implementation
The implementation phase consists of the following tasks: testing,
evaluation, demonstration, orientation, training and deployment.
Several of these tasks are performed simultaneously.
Testing Data on the system’s outputs are collected & compared against
the design specifications.
Evaluation The implemented system is evaluated to see how well it meets
user’s needs.
Technical & organizational loose ends are also identified.
Testing & evaluation usually result in changes in the design &
construction.
Demonstration Demonstration of the fully operational system capabilities to the
user community is an important phase.
Orientation This involves instruction of users in the basic capabilities &
operation of the system
Training Operational users are trained in system structure and functions.
Deployment The full system is operationally deployed for all members of the
user community.
Phase G : Maintenance and documentation
Maintenance involves planning for ongoing support of the system and
its user community.
Phase H : Adaptation
Adaptation requires recycling through the earlier steps on a regular
basis to respond to changing user needs and thus as several variables.
The voice input to the microphone produces an analog speech signal.
This speech signal is then converted into binary words compatible with
a digital computer by an analog to digital converter.
The binary version of the input voice is then stored in the system and
compared to previously store binary representations of words.
When a match occurs, recognition is achieved.
Then, the spoken work appears on a video screen or is passed along
to an NLP for meaning analysis.
A typical work recognizer
The voice input is applied to a microphone.
The electrical analog signal from the microphone is fed to an amplifier,
where it is increased in level.
The amplifier contains some kind of Automatic gain control to provide
an output signal in a specific voltage range.
The analog signal representing a spoken work is a complex waveform
that contains many individual frequencies.
The way to recognize the spoken work is to break that complex input
signal into its components parts.
This is usually done with a set of filters.
A filter is an electronic circuit that passes or rejects frequencies in a
certain range.
Voice synthesis
Voice synthesis or response is the technology by which computers
speak.
Tools and applications of voice technologies
Speech recognition is used in four major areas.
1. Command and control – including voice control of machine
operations and voice activated dialing
2. Data entry to forms – quality control system, or databases
3. Direction – including creation of documents and reports
4. Data access / Information retrieval – such as directory
assistance and retrieval of online data
Voice technology application sampler – Application
Research on User Interfaces in
Hwang and Wu identified the following indecent and dependent
variables.
Independent Variables
Human user Demographics – Age, education, experience
Psychological – Cognitive style, intelligence, risk attitude
Decision
environment
Decision structure, organizational level
Others – stability, time pressure, uncertainty
Task Decision support – complexity level
Inquiry / information retrieval , data entry, word processing and
Computer aided instruction
Interface
characteristics
Input / Output media, Dialog type, Presentation format and
Language characteristics
Dependent variable : Human / Computer effectiveness
This was measured by usefulness, perceived ease of use and
performance (time, error, task, completion, profit), user attributes
(satisfaction, confidence) and use of system option (high. low).
Development Strategies
Write a customized DSS in general purpose programming language
such as COBOL or PASCAL.
Use a fourth generation language (4GL). Such as data oriented
languages, spreadsheets, and financial oriented languages.
Use a DSS integrated development tool. An integrated package
eliminates the need to use multiple 4GLs. The best known examples
for the PC are Excel, Lotus 1-2-3, and Quattro Pro.
Use a domain specific DSS generator: Domain specific DSS
generators are designed to build a highly structured system, usually in
a functional area.
DSS Development Phase A : Planning Phase E : Construction
Phase B : Research Phase F : Implementation
Phase C : System analysis and
conceptual design
Phase G : Maintenance and
documentation
Phase D : Design Phase H : Adaptation
Phase A : Planning
Planning deals mainly with need assessment and problem diagnosis.
A crucial step in the planning effort is determining the key decisions to
be supported by the DSS.
Example _ in a portfolio selection system, a key decision might be
selecting the correct stocks for a particular customer’s needs.
Phase B : Research
This phase involves the identification of a relevant approach for
addressing user needs and available resources. (Hardware, software,
vendors, systems, studies or related experience in other organizations,
and review of relevant research).
Phase C : System analysis and conceptual design
This phase includes the determination of the best construction
approach and specific resources required implementing it, including
technical, staff, financial, and organizational resources.
Phase D : Design
The detailed specifications of the system components, structure and
features are determined.
Phase E : Construction
A DSS can be constructed in different ways depending on the design
philosophy and the tools being used.
The construction is the technical implementation of the design.
Phase F : Implementation
The implementation phase consists of the following tasks: testing,
evaluation, demonstration, orientation, training and deployment.
Several of these tasks are performed simultaneously.
Testing Data on the system’s outputs are collected & compared against
the design specifications.
Evaluation The implemented system is evaluated to see how well it meets
user’s needs.
Technical & organizational loose ends are also identified.
Testing & evaluation usually result in changes in the design &
construction.
Demonstration Demonstration of the fully operational system capabilities to the
user community is an important phase.
Orientation This involves instruction of users in the basic capabilities &
operation of the system
Training Operational users are trained in system structure and functions.
Deployment The full system is operationally deployed for all members of the
user community.
Phase G : Maintenance and documentation
Maintenance involves planning for ongoing support of the system and
its user community.
Phase H : Adaptation
Adaptation requires recycling through the earlier steps on a regular
basis to respond to changing user needs and thus as several variables.
Speech (Voice) recognition and Understanding
-recognition-.jpg)
Speech (Voice) recognition and
Speech or voice recognition is the process of having the computer
recognizes the normal human voice.
When a speech recognition system is combined with a natural
language processing system, the result is an overall system that not
only recognizes voice input but also understands it.
Advantages of speech recognition
Ease of access
(Typing skills)
Typing skills – many people may not be able to use computers
effectively
Speaking is easy then typing.
Speed
(speed in speaking
& typing)
Even the most competent typist can speak more quickly than
they can type.
Speed is faster in speaking than typing.
Manual freedom
(Hands free)
There are many situations in which computers might be useful to
people whose hands are otherwise occupied, such as product
assemblers’ pilot of military aircraft, and busy executives.
Remote access We can retrieve information by issuing verbal commands into a
(Verbal commands) telephone.
Accuracy
(Spelling mistakes)
Can reduce the spelling mistakes during typing.
These are minimized with voice input.
Classifying speech recognizers
There are systems that recognize only individual works and other that
recognize continuous speech.
The systems are further classified as either speaker dependent or
speaker independent.
1) Word recognizers 3) Speaker dependent
2) Continuous speech recognizers 4) Speaker independent
Word recognizers
A word recognizer is a speech recognition system that identifies
individual words.
Such systems are capable of recognizing only a small vocabulary of
single words or possibly simple phrases.
To give commands or to enter data to a computer using one of these
systems.
Continuous speck recognizers
These speech recognition units recognize a continuous flow of words.
We can speak to them in complete sentences, and our input will be
recognized and possibly understood.
Speaker dependent
A speaker dependent system must be customized to the voice of a
particular individual before it can be used.
Speaker independent
Speaker independent systems mean that anyone can use the system.
Most speaker independent systems are incredibly complete and costly.
Some of the most advanced systems are speech recognition system
and the Sphinx and Janus systems developed at Carnegie Mellon
University.
The Bell Labs system is used, for example, for airline reservations via
human phone input.
IBM’s "Voice Type Simply Speaking" is available for running Windows
95 and applications.
Speech or voice recognition is the process of having the computer
recognizes the normal human voice.
When a speech recognition system is combined with a natural
language processing system, the result is an overall system that not
only recognizes voice input but also understands it.
Advantages of speech recognition
Ease of access
(Typing skills)
Typing skills – many people may not be able to use computers
effectively
Speaking is easy then typing.
Speed
(speed in speaking
& typing)
Even the most competent typist can speak more quickly than
they can type.
Speed is faster in speaking than typing.
Manual freedom
(Hands free)
There are many situations in which computers might be useful to
people whose hands are otherwise occupied, such as product
assemblers’ pilot of military aircraft, and busy executives.
Remote access We can retrieve information by issuing verbal commands into a
(Verbal commands) telephone.
Accuracy
(Spelling mistakes)
Can reduce the spelling mistakes during typing.
These are minimized with voice input.
Classifying speech recognizers
There are systems that recognize only individual works and other that
recognize continuous speech.
The systems are further classified as either speaker dependent or
speaker independent.
1) Word recognizers 3) Speaker dependent
2) Continuous speech recognizers 4) Speaker independent
Word recognizers
A word recognizer is a speech recognition system that identifies
individual words.
Such systems are capable of recognizing only a small vocabulary of
single words or possibly simple phrases.
To give commands or to enter data to a computer using one of these
systems.
Continuous speck recognizers
These speech recognition units recognize a continuous flow of words.
We can speak to them in complete sentences, and our input will be
recognized and possibly understood.
Speaker dependent
A speaker dependent system must be customized to the voice of a
particular individual before it can be used.
Speaker independent
Speaker independent systems mean that anyone can use the system.
Most speaker independent systems are incredibly complete and costly.
Some of the most advanced systems are speech recognition system
and the Sphinx and Janus systems developed at Carnegie Mellon
University.
The Bell Labs system is used, for example, for airline reservations via
human phone input.
IBM’s "Voice Type Simply Speaking" is available for running Windows
95 and applications.
Geographic Information System (GIS)
Geographic Information System (GIS)
A GIS is a computer based system for capturing, storing, checking,
integrating, manipulating, and displaying data using digitized maps.
GIS has many applications.
GIS Software
GIS software varies in capabilities from simple computerized mapping
system to enterprise wide DSS tools for decision support data analysis.
A high quality graphic display and fast computation and search speeds
are necessary.
Many early GIS development were at universities and government and
military agencies.
GIS Data
The Census Bureau publishes a list of topologically integrated
Geographic Encoding and Referencing (TIGER) standard format, file
related product.
An alternative standard format, the Digital line Graph (DLG), was
developed by the US Geological Survey.
GIS and Decision Making
There are countless applications of GIS. Some of them as follow.
The dispatch of emergency vehicles, transit management, facility; site
selection, and wildlife management
Popular applications include political campaign support, consumer
marketing and sales support, sales and territory analysis, site selection,
fleet management, route planning, disaster planning, and regularity
compliance,.
The success of GIS is highly depended on good information. GIS are
proving to be a significant tactical tool for identifying product riches by
geography, facilitating presentations, improving comprehension,
identifying logistical problems, and developing marketing strategies.
Emerging GIS Applications
The integration of GIS and global positioning satellites (GPS) will
transform the aviation and shipping industries.
It enables vehicles or aircraft equipped with a GPS receiver to pinpoint
their locations as the move.
Emerging applications of GPS include personal automobile mapping
systems, railroad car tracking and earth moving equipment tracking.
Object linking and embedding (OLE) will allow users to import maps
into any document.
Natural Language Processing: An Overview
Natural Language Processing (NLP) is an applied artificial intelligence
technology.
It refers to communicating with a computer in English or whatever
language you speak.
To understand a natural language inquiry, a computer must have
knowledge to analyze and interpret the input.
This knowledge may include linguistic knowledge abut words, domain
knowledge, common sense knowledge and even knowledge about the
users and their goals.
NLP must understand grammar and the definitions of words.
NLP is an attempt to allow computers to interpret normal statement
expressed in a natural human language, such as English or Japanese.
The process of speech recognition, in contrast, attempts to translate
the human voice into individual words and sentences understandable
by the computer.
Natural Language Processing : Methods
With NLP, the computer’s understanding of human statements may or
may not be translated into a program.
The most advanced fourth generation languages today use the early
results of NLP research, but their abilities are still quite limited
comported with the ultimate goals of NLP.
Currently two major techniques are used in NLP programs.
1. Key word search (Pattern matching)
2. Language processing (Syntactic and semantic analysis)
Key word analysis (Pattern Matching)
In the pattern matching process, the NLP program searches through an
input sentence looking for selected key words or phrases.
Once a key work is recognized, the program activates a specific
canned response.
Language Processing (Syntactic, semantic and pragmatic analysis)
The most obvious and straightforward approach to the problem is to
perform a detailed analysis of the syntax and semantics of an input
statement.
An NLP can determine the meaning of the user’s query through
question such as "Do you mean to say that.
The procedures : How language processing works
The five major elements are …
1) The Parser This is the key element in NLP
It is software that syntactically analyses the input
sentence.
Each word is identified & its part of speech clarified.
The parser then maps the words into a structure called a
parse tree.
The parse tree shows the meanings of all the works &
how they are assembled.
2) The Lexicon The parser needs a directory called the Lexicon.
The lexicon contains all of the words that the program is
capable of recognizing.
o The parser and the lexicon work together to pick apart a sentence and then
create a parse tree.
o Once a parse tree has been constructed, the system is ready for semantic
analysis to obtain further meaning.
o Semantic analysis is the function of the Understander block.
3) The Understander This works in conjunction with the knowledge base to
determine what the sentence means.
The Understander creates another data structure that
represents the meaning and understanding of the
sentence and stores it in memory.
4) The Generator The above data structure, called a generator, can be used
to initiate addition action.
In its simplest form, the generator feeds standard
presorted output responses to the user based on the
meaning extracted from the input.
5) The knowledge base
Application of NLP and Software
NLP programs have been applied in several areas. The most important
are as follows.
Interfaces to databases and other software Grammar analysis
Abstracting and summarizing text Composing letters
Knowledge elicitation and machine learning Speech understanding
Translation of a natural language to another
natural language
EX _ English to German
Translation of a computer language to
another computer language
EX _ Fortran to C, the Unix command
f2c
A GIS is a computer based system for capturing, storing, checking,
integrating, manipulating, and displaying data using digitized maps.
GIS has many applications.
GIS Software
GIS software varies in capabilities from simple computerized mapping
system to enterprise wide DSS tools for decision support data analysis.
A high quality graphic display and fast computation and search speeds
are necessary.
Many early GIS development were at universities and government and
military agencies.
GIS Data
The Census Bureau publishes a list of topologically integrated
Geographic Encoding and Referencing (TIGER) standard format, file
related product.
An alternative standard format, the Digital line Graph (DLG), was
developed by the US Geological Survey.
GIS and Decision Making
There are countless applications of GIS. Some of them as follow.
The dispatch of emergency vehicles, transit management, facility; site
selection, and wildlife management
Popular applications include political campaign support, consumer
marketing and sales support, sales and territory analysis, site selection,
fleet management, route planning, disaster planning, and regularity
compliance,.
The success of GIS is highly depended on good information. GIS are
proving to be a significant tactical tool for identifying product riches by
geography, facilitating presentations, improving comprehension,
identifying logistical problems, and developing marketing strategies.
Emerging GIS Applications
The integration of GIS and global positioning satellites (GPS) will
transform the aviation and shipping industries.
It enables vehicles or aircraft equipped with a GPS receiver to pinpoint
their locations as the move.
Emerging applications of GPS include personal automobile mapping
systems, railroad car tracking and earth moving equipment tracking.
Object linking and embedding (OLE) will allow users to import maps
into any document.
Natural Language Processing: An Overview
Natural Language Processing (NLP) is an applied artificial intelligence
technology.
It refers to communicating with a computer in English or whatever
language you speak.
To understand a natural language inquiry, a computer must have
knowledge to analyze and interpret the input.
This knowledge may include linguistic knowledge abut words, domain
knowledge, common sense knowledge and even knowledge about the
users and their goals.
NLP must understand grammar and the definitions of words.
NLP is an attempt to allow computers to interpret normal statement
expressed in a natural human language, such as English or Japanese.
The process of speech recognition, in contrast, attempts to translate
the human voice into individual words and sentences understandable
by the computer.
Natural Language Processing : Methods
With NLP, the computer’s understanding of human statements may or
may not be translated into a program.
The most advanced fourth generation languages today use the early
results of NLP research, but their abilities are still quite limited
comported with the ultimate goals of NLP.
Currently two major techniques are used in NLP programs.
1. Key word search (Pattern matching)
2. Language processing (Syntactic and semantic analysis)
Key word analysis (Pattern Matching)
In the pattern matching process, the NLP program searches through an
input sentence looking for selected key words or phrases.
Once a key work is recognized, the program activates a specific
canned response.
Language Processing (Syntactic, semantic and pragmatic analysis)
The most obvious and straightforward approach to the problem is to
perform a detailed analysis of the syntax and semantics of an input
statement.
An NLP can determine the meaning of the user’s query through
question such as "Do you mean to say that.
The procedures : How language processing works
The five major elements are …
1) The Parser This is the key element in NLP
It is software that syntactically analyses the input
sentence.
Each word is identified & its part of speech clarified.
The parser then maps the words into a structure called a
parse tree.
The parse tree shows the meanings of all the works &
how they are assembled.
2) The Lexicon The parser needs a directory called the Lexicon.
The lexicon contains all of the words that the program is
capable of recognizing.
o The parser and the lexicon work together to pick apart a sentence and then
create a parse tree.
o Once a parse tree has been constructed, the system is ready for semantic
analysis to obtain further meaning.
o Semantic analysis is the function of the Understander block.
3) The Understander This works in conjunction with the knowledge base to
determine what the sentence means.
The Understander creates another data structure that
represents the meaning and understanding of the
sentence and stores it in memory.
4) The Generator The above data structure, called a generator, can be used
to initiate addition action.
In its simplest form, the generator feeds standard
presorted output responses to the user based on the
meaning extracted from the input.
5) The knowledge base
Application of NLP and Software
NLP programs have been applied in several areas. The most important
are as follows.
Interfaces to databases and other software Grammar analysis
Abstracting and summarizing text Composing letters
Knowledge elicitation and machine learning Speech understanding
Translation of a natural language to another
natural language
EX _ English to German
Translation of a computer language to
another computer language
EX _ Fortran to C, the Unix command
f2c
Graphical User Interface
Graphical User Interface
In this mode objects are visually represented as icons or symbols, are
directly manipulated by the user.
Hybrid Modes
Interface modes can be combined to provide improved functionality
and quality.
The user can point the mouse or cursor at the icon and use a
command to move it.
Graphics
Graphics enable the presentation of information in a way that more
clearly conveys the meaning of data and permits user to visualize
relationships.
The value of charts and graphs in the communication of numeric data
has long been recognized.
This information can even be animated.
Graphics Software
The primary purpose of graphics software is to present visual images
of information on a computer monitor, a printer / plotter.
Graphics software can be a standalone package that allows managers
to create graphic output directly from databases or spreadsheets in a
no technical and user friendly way.
Representative graphic software includes Harvard Graphics, Power
Point, SAS Graph, Lotus/Freelane, Draw Perfect, and Tell-a-graph.
The role of computer graphics
Text plays a critical role in graphics.
Time series charts show the value of one or more variables over time.
Bar and Pie charts can be use to show total values.
Scatter diagrams show the relationship between two variables.
Maps can be two or three dimensional.
Layouts of rooms, buildings or shipping centers convey much
information in simple diagrams.
Hierarchy charts _ Organizational charts are widely used.
Sequence charts _ Flowcharts, show the necessary; sequence of
events and which activities can be done in parallel.
Motion graphics _ motion pictures and television, clearly will continue
to perform vital functions.
Desktop publishing systems _ have extensive graphic capabilities such
as transferring a picture into the computer.
Multimedia
Computerized systems employ several multimedia technologies as
presentation devices.
Multimedia refers to a pool of human machine communication media.
One class of multimedia is called hypermedia.
VRML – Virtual Reality Markup Language
Hypermedia
Hypermedia describes documents that could contain several types of
media, which allow information to be linked by association.
Hypermedia is used as a presentation tool used for knowledge and
data navigation.
Examples of commercial products include Hyper Card and Note Cards,
Hypermedia can contain several layers of information, such as…
A menu based natural
language interfaces
To provide a simple & transparent way for users to rum the
system and query it.
An object oriented
database
That permits concurrent access to its data structure and
operations.
A relational query
interface
That can efficiently support complex queries.
A hypermedia abstract
machine
That lets user link different types of information.
Media editors That provides way to view and edit text, graphics, images, and
voice.
A change
management vital
memory
To manage temporary versions, configurations, and
transformations of design entities.
Hypertext
Hypertext is an approach for handling text and graphic information that
allow the user to jump from a given topic, wherever they wish, to
related ideas.
Hypertext allows user to access information in a nonlinear fashion b
following sequence of thought.
It lets the reader control the level of details and the type of information
displayed.
It allows a quick search according to the reader’s interest.
In this mode objects are visually represented as icons or symbols, are
directly manipulated by the user.
Hybrid Modes
Interface modes can be combined to provide improved functionality
and quality.
The user can point the mouse or cursor at the icon and use a
command to move it.
Graphics
Graphics enable the presentation of information in a way that more
clearly conveys the meaning of data and permits user to visualize
relationships.
The value of charts and graphs in the communication of numeric data
has long been recognized.
This information can even be animated.
Graphics Software
The primary purpose of graphics software is to present visual images
of information on a computer monitor, a printer / plotter.
Graphics software can be a standalone package that allows managers
to create graphic output directly from databases or spreadsheets in a
no technical and user friendly way.
Representative graphic software includes Harvard Graphics, Power
Point, SAS Graph, Lotus/Freelane, Draw Perfect, and Tell-a-graph.
The role of computer graphics
Text plays a critical role in graphics.
Time series charts show the value of one or more variables over time.
Bar and Pie charts can be use to show total values.
Scatter diagrams show the relationship between two variables.
Maps can be two or three dimensional.
Layouts of rooms, buildings or shipping centers convey much
information in simple diagrams.
Hierarchy charts _ Organizational charts are widely used.
Sequence charts _ Flowcharts, show the necessary; sequence of
events and which activities can be done in parallel.
Motion graphics _ motion pictures and television, clearly will continue
to perform vital functions.
Desktop publishing systems _ have extensive graphic capabilities such
as transferring a picture into the computer.
Multimedia
Computerized systems employ several multimedia technologies as
presentation devices.
Multimedia refers to a pool of human machine communication media.
One class of multimedia is called hypermedia.
VRML – Virtual Reality Markup Language
Hypermedia
Hypermedia describes documents that could contain several types of
media, which allow information to be linked by association.
Hypermedia is used as a presentation tool used for knowledge and
data navigation.
Examples of commercial products include Hyper Card and Note Cards,
Hypermedia can contain several layers of information, such as…
A menu based natural
language interfaces
To provide a simple & transparent way for users to rum the
system and query it.
An object oriented
database
That permits concurrent access to its data structure and
operations.
A relational query
interface
That can efficiently support complex queries.
A hypermedia abstract
machine
That lets user link different types of information.
Media editors That provides way to view and edit text, graphics, images, and
voice.
A change
management vital
memory
To manage temporary versions, configurations, and
transformations of design entities.
Hypertext
Hypertext is an approach for handling text and graphic information that
allow the user to jump from a given topic, wherever they wish, to
related ideas.
Hypertext allows user to access information in a nonlinear fashion b
following sequence of thought.
It lets the reader control the level of details and the type of information
displayed.
It allows a quick search according to the reader’s interest.
Knowledge in Artificial Intelligence
Genetic Algorithms
Genetic algorithms have been applied to many large scale
combinatorial mathematical programming problems such as large scale
scheduling problems and even to producing police sketches of
criminals.
Knowledge in Artificial Intelligence
As per Sowa, "Knowledge encompasses the implicit and explicit
restrictions placed upon objects, operations, and relationship along with
general and relationships along with general and specific heuristics and
inference procedures involved in the situation being modeled.
Knowledge is now recognized as a major organization resource. Data,
information, and knowledge can be classified by their degree of
abstraction and by their quantity.
Knowledge is the most abstract and exist in the smallest quantity.
Uses of Knowledge
It can use knowledge given to it b human experts. Such knowledge
consists of facts, concepts, theories, heuristic methods, procedures
and relationships.
The collection of knowledge related to a problem used in AI system is
organized together and it is called a knowledge base.
Once a knowledge base is built, AI techniques are used to give the
computer inference capability based on the facts and relationships
contained in the knowledge base.
Types of Knowledge based Decision support system
The knowledge component enables a wider range of decisions.
It extends the capabilities of computers well beyond based and model
based DSS.
Intelligent decision support system
Several models of intelligent DSS were developed over the years.
Active (Symbiotic) DSS
The DSS executes computations, presents data, and responds to
standard commands.
The DSS should be able to take initiative, or should be able to respond
to nonstandard requests and commands. This type of DSS is called
active or symbiotic DSS.
User Interface and Decision Visualization
The key to successful use of any DSS is the user interface.
The user interface is the hardware and software that facilitate
communication and interaction between the user and the computer.
User interface area, the subset of the field called human computer
interaction, which is the study of the people, computer technology and the
ways these interact.
The interface includes responses and involves an exchange of graphics,
acoustics, tactile and other means of communication.
Physical aspects of the user interface include…
Input Device _ Mouse, Microphone or keyboard
Output (Display) Device… Monitor, Printer or Speaker
The user formulates a response and takes an action.
The cyclic process shown consists of the following components.
Knowledge It is the information the user must have communicate with the
computer.
Dialog It is an observable series of interchanges or interactions
between the user and the computer.
Action Language This can take various forms, ranging from selecting an item from
the menu to answering a question, moving a display window or
typing command.
Input devices are used to execute actions.
Computer This interprets the user’s action, executes a task and generates
a display.
Presentation
Language
This is the information displayed to the user via output devices.
Such information can be shown as display menus, windows or
texts.
It can be static or dynamic, numeric or symbolic.
It can appear visually on the monitor, presented as voice or a
printout.
User reaction The user interprets the display, processes the content and plans
an action.
The following are the some of the important issues in building a users
interface. These issues are handled in user interface management system
(UIMS).
Choice of the input & output devices Information density
Screen design Use of icons and symbols
Human machine interaction sequences Information display format
Use of colors and shading
User Interface Management System (UIMS)
The UIMS also accommodates the action language that enables the
computer inputs and outputs in the form of dialing language or processes.
Interface Models / Styles
The combination of presentation and action language is called interface
mode.
The interface model determines how information is entered and
displayed.
Following are the different models.
1) Menu Interaction 6) Neural Language
2) Pull down menus 7) Graphical user interface
3) Command language 8) Hybrid modes
4) Question and Answers 9) Graphics
5) Form Interaction 10) Graphic software
Menu Interaction
In this mode the user selects an item from a list of possible choices for
the function to be performed.
A menu can have sub menus.
Example_ Menu such as file, edit, view, etc.
Pull down menus
A pull down menu is a submenu that appears as superimposed drop
down menu on the screen usually after an entry has been made in a
higher level menu.
Windows based software makes extensive use of pull down menus.
Command languages
In this the user enters a command such as run or plot.
Many commands are composed of verb-noun combination such as plot
scales.
Another way to simplify commands is to use macros.
Commands can also entered by voice.
Question & Answers
This mode begins with the computer asking the user a question.
The user answers the question with a phrase or a sentence or by
selecting an icon in the menu.
Expert systems are the best example for this type.
Form interaction
In this the user enters data or commands into designated space called
forms.
Natural Language
A human computer interaction that is similar to human dialog is called
natural language.
Genetic algorithms have been applied to many large scale
combinatorial mathematical programming problems such as large scale
scheduling problems and even to producing police sketches of
criminals.
Knowledge in Artificial Intelligence
As per Sowa, "Knowledge encompasses the implicit and explicit
restrictions placed upon objects, operations, and relationship along with
general and relationships along with general and specific heuristics and
inference procedures involved in the situation being modeled.
Knowledge is now recognized as a major organization resource. Data,
information, and knowledge can be classified by their degree of
abstraction and by their quantity.
Knowledge is the most abstract and exist in the smallest quantity.
Uses of Knowledge
It can use knowledge given to it b human experts. Such knowledge
consists of facts, concepts, theories, heuristic methods, procedures
and relationships.
The collection of knowledge related to a problem used in AI system is
organized together and it is called a knowledge base.
Once a knowledge base is built, AI techniques are used to give the
computer inference capability based on the facts and relationships
contained in the knowledge base.
Types of Knowledge based Decision support system
The knowledge component enables a wider range of decisions.
It extends the capabilities of computers well beyond based and model
based DSS.
Intelligent decision support system
Several models of intelligent DSS were developed over the years.
Active (Symbiotic) DSS
The DSS executes computations, presents data, and responds to
standard commands.
The DSS should be able to take initiative, or should be able to respond
to nonstandard requests and commands. This type of DSS is called
active or symbiotic DSS.
User Interface and Decision Visualization
The key to successful use of any DSS is the user interface.
The user interface is the hardware and software that facilitate
communication and interaction between the user and the computer.
User interface area, the subset of the field called human computer
interaction, which is the study of the people, computer technology and the
ways these interact.
The interface includes responses and involves an exchange of graphics,
acoustics, tactile and other means of communication.
Physical aspects of the user interface include…
Input Device _ Mouse, Microphone or keyboard
Output (Display) Device… Monitor, Printer or Speaker
The user formulates a response and takes an action.
The cyclic process shown consists of the following components.
Knowledge It is the information the user must have communicate with the
computer.
Dialog It is an observable series of interchanges or interactions
between the user and the computer.
Action Language This can take various forms, ranging from selecting an item from
the menu to answering a question, moving a display window or
typing command.
Input devices are used to execute actions.
Computer This interprets the user’s action, executes a task and generates
a display.
Presentation
Language
This is the information displayed to the user via output devices.
Such information can be shown as display menus, windows or
texts.
It can be static or dynamic, numeric or symbolic.
It can appear visually on the monitor, presented as voice or a
printout.
User reaction The user interprets the display, processes the content and plans
an action.
The following are the some of the important issues in building a users
interface. These issues are handled in user interface management system
(UIMS).
Choice of the input & output devices Information density
Screen design Use of icons and symbols
Human machine interaction sequences Information display format
Use of colors and shading
User Interface Management System (UIMS)
The UIMS also accommodates the action language that enables the
computer inputs and outputs in the form of dialing language or processes.
Interface Models / Styles
The combination of presentation and action language is called interface
mode.
The interface model determines how information is entered and
displayed.
Following are the different models.
1) Menu Interaction 6) Neural Language
2) Pull down menus 7) Graphical user interface
3) Command language 8) Hybrid modes
4) Question and Answers 9) Graphics
5) Form Interaction 10) Graphic software
Menu Interaction
In this mode the user selects an item from a list of possible choices for
the function to be performed.
A menu can have sub menus.
Example_ Menu such as file, edit, view, etc.
Pull down menus
A pull down menu is a submenu that appears as superimposed drop
down menu on the screen usually after an entry has been made in a
higher level menu.
Windows based software makes extensive use of pull down menus.
Command languages
In this the user enters a command such as run or plot.
Many commands are composed of verb-noun combination such as plot
scales.
Another way to simplify commands is to use macros.
Commands can also entered by voice.
Question & Answers
This mode begins with the computer asking the user a question.
The user answers the question with a phrase or a sentence or by
selecting an icon in the menu.
Expert systems are the best example for this type.
Form interaction
In this the user enters data or commands into designated space called
forms.
Natural Language
A human computer interaction that is similar to human dialog is called
natural language.
Artificial Intelligence (AI)
Artificial Intelligence (AI)
AI concerned with two basic ideas.
First, it involves studying the thought processes of humans.
Second, it deals with representing those processes via machines (such as
computers and robots)
AI is behavior by a machine that, if performed by a human being, would be
called intelligent.
As per Rich and Knight, AI is the study of how to make computers do
things at which, at the moment, people are better.
As per Mark Fox of Carnegie-Mellon University, AI is basically a theory of
how the human mind works.
As per Winston and Predergast list 3 objectives of artificial intelligence.
o Make machines smarter – Primary goal
o Understand what intelligence is – The noble laureate purpose
o Make machines more useful – the entrepreneurial purpose
Several abilities are reconsidered signs of intelligence
Testing for intelligence
Turing Test
According to this test, a computer cold is considered to be smart only
when a human interviewer, conversing with both an unseen human being
and an unseen computer, could not determine which is which.
Symbolic Processing
They choose symbols to represent the problem concepts and apply
various strategies and rules to manipulate these concepts.
Some examples of symbols.
Product …….Defendant………0.8………..Chocolate
AI is the branch of computer science dealing primarily with symbolic, non
algorithmic methods of problem solving.
Two characteristics…….
1. Numeric versus symbolic
2. Algorithmic versus non algorithmic
Heuristics
Heuristics are the key element of AI.
Often heuristics are used to limit the search and focus on the most
promising areas.
Inference
AI is unique in that it makes inferences by using a pattern matching
approach.
Pattern matching
AI works with pattern matching methods that attempt to describe objects,
events, or processes in terms of their qualitative features and logical and
computational relationships.
Artificial Intelligence Versus Natural Intelligence
Artificial Intelligence Field
AI comes together in the area of logic, philosophy of language and
philosophy of mind.
Mutual interaction between electrical engineering and AI include image
processing, control theory, pattern recognition and robotics.
AI provides scientific foundation for several growing commercial
technologies.
The major areas are …
1) Expert Systems 6) Neural computing
2) Natural language processing 7) Automatic programming
3) Speech & voice understanding 8) Fuzzy logic
4) Robotics & sensory systems 9) Generic algorithms
5) Computer vision & Sense recognition 10) Intelligent agents
Expert System - EI
Es are computerized programs that attempt to imitate the reasoning
processes and knowledge of experts I solving the specific problems.
Natural Language Processing - NLP
Natural language technology gives computer users the ability to
communicate with the computer in their native language.
The field of NLP consists of two sub fields.
Speech and Voice understanding
Speech understanding is the recognition and understanding by a
computer of spoken language.
Robotics and Sensory System
Sensory systems such as vision systems, tactile systems and signal
processing system when combines with AI, defined a broad category of
systems generally called Robotics.
A robot is an electromechanical device that can be programmed to
perform manual tasks.
The intelligent robot allows it to interpret the collected information and
to respond and adapt to changes in its environment.
Robots combine sensory systems with mechanical motion to produce
machines of widely varying intelligence and abilities.
Computer vision and sense recognition
This has been defined as the addition of some form of computer
intelligence and decision making to digitize visual information received
from a machine sensor such as camera.
The combined information is then used to perform or control such
operations as robotic movement, conveyor speeds and production line
quality.
The basic objective of computer vision is to interpret scenarios rather
than generate pictures.
Neural computing
A neural network is a mathematical model of the way a brain functions.
Other applications of AI include automatic programming, fuzzy logic
genetic algorithms and intelligent agent.
Automatic programming
The goal of automatic programming is to create special programs that
act as intelligent tools to assist programmers and expedite each phase
of the programming process.
The ultimate aim of automatic programming is a computer system that
could develop programs by itself.
Fuzzy logic
This extends the notions of logic beyond simple true/false to allow for
partial or even continuous truths.
AI concerned with two basic ideas.
First, it involves studying the thought processes of humans.
Second, it deals with representing those processes via machines (such as
computers and robots)
AI is behavior by a machine that, if performed by a human being, would be
called intelligent.
As per Rich and Knight, AI is the study of how to make computers do
things at which, at the moment, people are better.
As per Mark Fox of Carnegie-Mellon University, AI is basically a theory of
how the human mind works.
As per Winston and Predergast list 3 objectives of artificial intelligence.
o Make machines smarter – Primary goal
o Understand what intelligence is – The noble laureate purpose
o Make machines more useful – the entrepreneurial purpose
Several abilities are reconsidered signs of intelligence
Testing for intelligence
Turing Test
According to this test, a computer cold is considered to be smart only
when a human interviewer, conversing with both an unseen human being
and an unseen computer, could not determine which is which.
Symbolic Processing
They choose symbols to represent the problem concepts and apply
various strategies and rules to manipulate these concepts.
Some examples of symbols.
Product …….Defendant………0.8………..Chocolate
AI is the branch of computer science dealing primarily with symbolic, non
algorithmic methods of problem solving.
Two characteristics…….
1. Numeric versus symbolic
2. Algorithmic versus non algorithmic
Heuristics
Heuristics are the key element of AI.
Often heuristics are used to limit the search and focus on the most
promising areas.
Inference
AI is unique in that it makes inferences by using a pattern matching
approach.
Pattern matching
AI works with pattern matching methods that attempt to describe objects,
events, or processes in terms of their qualitative features and logical and
computational relationships.
Artificial Intelligence Versus Natural Intelligence
Artificial Intelligence Field
AI comes together in the area of logic, philosophy of language and
philosophy of mind.
Mutual interaction between electrical engineering and AI include image
processing, control theory, pattern recognition and robotics.
AI provides scientific foundation for several growing commercial
technologies.
The major areas are …
1) Expert Systems 6) Neural computing
2) Natural language processing 7) Automatic programming
3) Speech & voice understanding 8) Fuzzy logic
4) Robotics & sensory systems 9) Generic algorithms
5) Computer vision & Sense recognition 10) Intelligent agents
Expert System - EI
Es are computerized programs that attempt to imitate the reasoning
processes and knowledge of experts I solving the specific problems.
Natural Language Processing - NLP
Natural language technology gives computer users the ability to
communicate with the computer in their native language.
The field of NLP consists of two sub fields.
Speech and Voice understanding
Speech understanding is the recognition and understanding by a
computer of spoken language.
Robotics and Sensory System
Sensory systems such as vision systems, tactile systems and signal
processing system when combines with AI, defined a broad category of
systems generally called Robotics.
A robot is an electromechanical device that can be programmed to
perform manual tasks.
The intelligent robot allows it to interpret the collected information and
to respond and adapt to changes in its environment.
Robots combine sensory systems with mechanical motion to produce
machines of widely varying intelligence and abilities.
Computer vision and sense recognition
This has been defined as the addition of some form of computer
intelligence and decision making to digitize visual information received
from a machine sensor such as camera.
The combined information is then used to perform or control such
operations as robotic movement, conveyor speeds and production line
quality.
The basic objective of computer vision is to interpret scenarios rather
than generate pictures.
Neural computing
A neural network is a mathematical model of the way a brain functions.
Other applications of AI include automatic programming, fuzzy logic
genetic algorithms and intelligent agent.
Automatic programming
The goal of automatic programming is to create special programs that
act as intelligent tools to assist programmers and expedite each phase
of the programming process.
The ultimate aim of automatic programming is a computer system that
could develop programs by itself.
Fuzzy logic
This extends the notions of logic beyond simple true/false to allow for
partial or even continuous truths.
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