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.
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