Expert systems 1
- 2. 2 What is an expert system? “An expert system is a computer system that emulates, or acts in all respects, with the decision-making capabilities of a human expert.” Professor Edward Feigenbaum Stanford University
- 3. 3 Areas of Artificial Intelligence
- 4. 4 Expert system technology may include: • Special expert system languages – CLIPS • Programs • Hardware designed to facilitate the implementation of those systems
- 5. 5 Expert System Main Components • Knowledge base – obtainable from books, magazines, knowledgeable persons, etc. • Inference engine – draws conclusions from the knowledge base
- 6. 6 Basic Functions of Expert Systems
- 7. 7 Problem Domain vs. Knowledge Domain • An expert’s knowledge is specific to one problem domain – medicine, finance, science, engineering, etc. • The expert’s knowledge about solving specific problems is called the knowledge domain. • The problem domain is always a superset of the knowledge domain.
- 8. 8 Problem and Knowledge Domain Relationship
- 9. 9 Advantages of Expert Systems • Increased availability • Reduced cost • Reduced danger • Performance • Multiple expertise • Increased reliability
- 10. 10 Advantages Continued • Explanation • Fast response • Steady, unemotional, and complete responses at all times • Intelligent tutor • Intelligent database
- 11. 11 Representing the Knowledge The knowledge of an expert system can be represented in a number of ways, including IF- THEN rules: IF you are hungry THEN eat
- 12. 12 Knowledge Engineering The process of building an expert system: 1. The knowledge engineer establishes a dialog with the human expert to elicit knowledge. 2. The knowledge engineer codes the knowledge explicitly in the knowledge base. 3. The expert evaluates the expert system and gives a critique to the knowledge engineer.
- 13. 13 Development of an Expert System
- 14. 14 The Role of AI • An algorithm is an ideal solution guaranteed to yield a solution in a finite amount of time. • When an algorithm is not available or is insufficient, we rely on artificial intelligence (AI). • Expert system relies on inference – we accept a “reasonable solution.”
- 15. 15 Uncertainty • Both human experts and expert systems must be able to deal with uncertainty. • It is easier to program expert systems with shallow knowledge than with deep knowledge. • Shallow knowledge – based on empirical and heuristic knowledge. • Deep knowledge – based on basic structure, function, and behavior of objects.
- 16. 16 Limitations of Expert Systems • Typical expert systems cannot generalize through analogy to reason about new situations in the way people can. • A knowledge acquisition bottleneck results from the time-consuming and labor intensive task of building an expert system.
- 17. 17 Early Expert Systems • DENDRAL – used in chemical mass spectroscopy to identify chemical constituents • MYCIN – medical diagnosis of illness • DIPMETER – geological data analysis for oil • PROSPECTOR – geological data analysis for minerals • XCON/R1 – configuring computer systems
- 18. 18 Table 1.3 Broad Classes of Expert Systems
- 19. 19 Problems with Algorithmic Solutions • Conventional computer programs generally solve problems having algorithmic solutions. • Algorithmic languages include C, Java, and C#. • Classic AI languages include LISP and PROLOG.
- 20. 20 Considerations for Building Expert Systems • Can the problem be solved effectively by conventional programming? • Is there a need and a desire for an expert system? • Is there at least one human expert who is willing to cooperate? • Can the expert explain the knowledge to the knowledge engineer can understand it. • Is the problem-solving knowledge mainly heuristic and uncertain?
- 21. 21 Elements of an Expert System • User interface – mechanism by which user and system communicate. • Working memory – global database of facts used by rules. • Inference engine – makes inferences deciding which rules are satisfied and prioritizing.
- 22. 22 Elements Continued • Agenda – a prioritized list of rules created by the inference engine, whose patterns are satisfied by facts or objects in working memory. • Knowledge Base – the set of rules, regulations and the information regarding the research are or problem area
- 23. 23 Production Rules • Knowledge base is also called production memory. • Production rules can be expressed in IF-THEN pseudocode format. • In rule-based systems, the inference engine determines which rule antecedents are satisfied by the facts.
- 24. 24 Structure of a Rule-Based Expert System
- 25. 25 Summary • During the 20th Century various definitions of AI were proposed. • In the 1960s, a special type of AI called expert systems dealt with complex problems in a narrow domain, e.g., medical disease diagnosis. • Today, expert systems are used in a variety of fields. • Expert systems solve problems for which there are no known algorithms.
- 26. 26 Summary Continued • Expert systems are knowledge-based – effective for solving real-world problems. • Expert systems are not suited for all applications. • Future advances in expert systems will hinge on the new quantum computers and those with massive computational abilities in conjunction with computers on the Internet.