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R. W

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INFO 629 Concepts in Artificial Intelligence

Expert Systems

Fall 2004

Professor: Dr. Rosina Weber

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Highlights• Concept• Methodology• Knowledge and reasoning• Knowledge representation• Forward, backward chaining • ES and AI tasks• Maintenance• Knowledge acquisition• Limited, bounded domains• Use of shells• Advantages/disadvantages of ES

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Expert Systems

• Computer systems that can perform expert tasks.(general, vague)

• A methodology that manipulates explicit knowledge with an inference engine to perform AI tasks.

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the concept

knowledgebase

(e.g.,framesand methods)

knowledgebase

(e.g.,framesand methods)

expertproblemexpert

problem

inferenceengine

(agenda)

inferenceengine

(agenda) expertsolutionexpert

solution

knowledge

reasoning

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expertsolutionexpert

solution

The complete methodology

knowledgebase

(e.g.,framesand methods)

knowledgebase

(e.g.,framesand methods)

explanationexplanation

generalknowledgegeneral

knowledge

userInterface

userInterface

expertproblemexpert

probleminferenceengine

(agenda)

inferenceengine

(agenda)

working memory(short-term mem/information)

working memory(short-term mem/information)

Knowledge acquisitionKnowledge acquisition

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Expert Systems

• Knowledge and • reasoning

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Knowledge representation formalisms

• (production) rules• frames (concepts, objects,

facts)• belief networks• methods• object-oriented• semantic nets• logic

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Inference Engines

•Forward chaining–Analysis, multiple outcomes

•Backward chaining–Attempt to test limited number of hypotheses

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Maintenance

• Maintenance focus on knowledge• Complexity of inter-relations among

rules• Difficult to automate maintenance

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Knowledge acquisition

• From several human experts– Unstructured interviews– Structured interviews– Methods learned from psychology

• Automated through machine learning methods

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Domains

• Limited, bounded domains

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ES Shells

• Easy prototyping to test ideas• KAPPA PC• CLIPS

• Examples in KAPPA PC

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ES and AI tasks

•From: Durkin, J. (1994). Expert Systems: design and development. Prentice-Hall, Inc., New Jersey.

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advantages (i)

• Permanence of knowledge - Expert systems do not forget or retire or quit, but human experts may

• Breadth - One ES can (and should) entail knowledge learned from an unlimited number of human experts.

• Reproducibility - Many copies of an expert system can be made, but training new human experts is time-consuming and expensive.

• Timeliness - Fraud and/or errors can be prevented. Information is available sooner for decision making

• Entry barriers - Expert systems can help a firm create entry barriers for potential competitors

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advantages (ii)

• Efficiency - can increase throughput and decrease personnel costs

• Although expert systems may be expensive to build and maintain, they are inexpensive to operate

• If there is a maze of rules (e.g. tax and auditing), then the expert system can "unravel" the maze

• Development and maintenance costs can be spread over many users

• The overall cost can be quite reasonable when compared to expensive and scarce human experts

• Cost savings, e.g., wages, minimize loan loss, reduce customer support effort

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advantages (iii)

• Documentation - An expert system can provide permanent documentation of the decision process

• Increased availability: the mass production of expertise• Completeness - An expert system can review all the

transactions, a human expert can only review a sample; an ES solution will always be complete and deterministic

• Consistency - With expert systems similar transactions handled in the same way. Humans are influenced by recency effects and primacy effects (early information dominates the judgment).

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advantages (iv)• Differentiation - In some cases, an expert system can

differentiate a product or can be related to the focus of the firm• Reduced danger: ES can be used in any environment• Reliability: ES will keep working properly regardless of of

external conditions that may cause stress to humans• Explanation: ES can trace back their reasoning providing

justification, increasing the confidence that the correct decision was made

• Indirect advantage is that the development of an ES requires that knowledge and processes are verified for correctness, completeness, and consistency.

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disadvantages • Common sense - In addition to a great deal of technical

knowledge, human experts have common sense. To program common sense in an ES, you must acquire and represent rules, which is not feasible.

• Creativity - Human experts can respond creatively to unusual situations, expert systems cannot.

• Learning - Human experts automatically adapt to changing environments; expert systems must be explicitly updated.

• Complexity and interrelations of rules grow exponentially as more rules are added.

• Sensory Experience - Human experts have available to them a wide range of sensory experience; expert systems are currently dependent on symbolic input.

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disadvantages (ii) • Degradation - Expert systems are not good at recognizing when

no answer exists or when the problem is outside their area of expertise. So, ES may provide a solution that is not optimal like one that is optimal

• High knowledge engineering requirements: In many real world domains, the amount of knowledge necessary to cover an expert problem is abundant making ES development time-consuming and complex

• Knowledge acquisition bottleneck• Difficulty to deal with imprecision (I.e., incompleteness, ,

uncertainty, ignorance, ambiguity)

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Necessary grounds for computer understanding

• Ability to represent knowledge and reason with it.

• Perceive equivalences and analogies between two different representations of the same entity/situation.

• Learning and reorganizing new knowledge.– From Peter Jackson (1998) Introduction to Expert systems.

Addison-Wesley third edition. Chapter 2, page 27.