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

1. INFO 629 Concepts in Artificial IntelligenceExpert SystemsFall 2004Professor: Dr. Rosina Weber

2. 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

3. 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.

4. the concept knowledge expertproblem reasoning knowledgebase (e.g.,framesand methods) inferenceengine (agenda) expertsolution

5. The complete methodology Knowledge acquisition working memory (short-term mem/information) expertproblem knowledgebase (e.g.,framesand methods) userI n t e r f a c e inferenceengine (agenda) expertsolution explanation generalknowledge

6. Expert Systems • Knowledge and • reasoning

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

8. Inference Engines • Forward chaining • Analysis, multiple outcomes • Backward chaining • Attempt to test limited number of hypotheses

9. Maintenance • Maintenance focus on knowledge • Complexity of inter-relations among rules • Difficult to automate maintenance

10. Knowledge acquisition • From several human experts • Unstructured interviews • Structured interviews • Methods learned from psychology • Automated through machine learning methods

11. Domains • Limited, bounded domains

12. ES Shells • Easy prototyping to test ideas • KAPPA PC • CLIPS • Examples in KAPPA PC

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

14. 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

15. 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

16. 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).

17. 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.

18. 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.

19. 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)

20. 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.