Copyright R. Weber INFO 629 Concepts in Artificial Intelligence Expert Systems Fall 2004 Professor: Dr. Rosina Weber

Copyright R. Weber 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

Copyright R. Weber 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.

Copyright R. Weber the concept knowledge base (e.g.,frames and methods) knowledge base (e.g.,frames and methods) expert problem inference engine (agenda) inference engine (agenda) expert solution knowledge reasoning

Copyright R. Weber expert solution The complete methodology knowledge base (e.g.,frames and methods) knowledge base (e.g.,frames and methods) explanation general knowledge user I n t e r f a c e user I n t e r f a c e expert problem inference engine (agenda) inference engine (agenda) working memory ( short-term mem/information ) working memory ( short-term mem/information ) Knowledge acquisition

Copyright R. Weber Expert Systems Knowledge and reasoning

Copyright R. Weber Knowledge representation formalisms (production) rules frames (concepts, objects, facts) belief networks methods object-oriented semantic nets logic

Copyright R. Weber Inference Engines Forward chaining –Analysis, multiple outcomes Backward chaining –Attempt to test limited number of hypotheses

Copyright R. Weber Maintenance Maintenance focus on knowledge Complexity of inter-relations among rules Difficult to automate maintenance

Copyright R. Weber Knowledge acquisition From several human experts –Unstructured interviews –Structured interviews –Methods learned from psychology Automated through machine learning methods

Copyright R. Weber Domains Limited, bounded domains

Copyright R. Weber ES Shells Easy prototyping to test ideas KAPPA PC CLIPS Examples in KAPPA PC

Copyright R. Weber ES and AI tasks From: Durkin, J. (1994). Expert Systems: design and development. Prentice- Hall, Inc., New Jersey.

Copyright R. Weber 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

Copyright R. Weber 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

Copyright R. Weber 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).

Copyright R. Weber 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.

Copyright R. Weber 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.

Copyright R. Weber 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)

Copyright R. Weber 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.