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.
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