1. Lecture 19

2. Recap Human Expert vs. Expert System Why Expert Systems? –Increase Availability –Reduce Cost –Permanence –Expertise drawn from multiple sources –Reliability –Fast Response –Steady and consistent –Explanation facility How are expert systems used (replace, assist) Examples of expert systems: SMARTPlan, R1/XCON, MYCIN

3. Application Areas Medicine (MYCIN) Manufacturing Business (SMARTPlan) Engineering (R1/XCON) PEACE, CAD tool to assist in design of electronic structures. Agriculture Education (GUIDON)

4. Expert System Structure Lets look at how an expert (say a doctor) solves a problem: –Focused area of expertise –Specialized Knowledge (Long-term Memory, LTM) –Case facts (Short-term Memory, STM) –Reasons with these to form new knowledge –Solves the given problem Lets define the corresponding concepts in an Expert System.

5. Expert System Structure ConclusionsSolution Inference EngineReasoning Case/Inferred Facts(stored in Working Memory) Case Facts (stored in STM) Domain Knowledge (stored in Knowledge Base) Specialized Knowledge (stored in LTM) DomainFocused Area of Expertise Expert SystemHuman Expert

6. Expert System Structure USER Working Memory Analogy: STM -Initial Case facts -Inferred facts Knowledge Base Analogy: LTM - Domain Knowedge Inference Engine Expert System

7. Knowledge Base Part of the expert system that contains the domain knowledge –Problem facts, Rules –Concepts –Relationships One of the roles of the expert system designer is to act as a knowledge engineer. Knowledge acquisition bottleneck You have to get information from the expert and encode it in the knowledge base, using one of the knowledge representation techniques we discussed in KRR. As discussed, one way of encoding that knowledge is in the form of IF-THEN rules. We saw that such representation is especially conducive to reasoning.

8. Working Memory ‘Part of the expert system that contains the problem facts that are discovered during the session’ Durkin A session is one consultation. During a consultation: –User presents some facts about the situation. –These are stored in the working memory. –Using these and the knowledge in the knowledge base, new information is inferred and also added to the working memory.

9. Inference Engine Processor in an expert system that matches the facts contained in the working memory with the domain knowledge contained in the knowledge base, to draw conclusions about the problem The inference engine works with the knowledge base and the working memory, and draws on both to add new facts to the working memory. If our knowledge is represented in the form of IF-THEN rules, the Inference Engine has the following mechanism –Match given facts in working memory to the premises of the rules in the knowledge base, if match found, ‘fire’ the conclusion of the rule, i.e. add the conclusion to the working memory.

10. Expert System Example: Family Knowledge Base Rule 1: IF father (X, Y) AND father (X, Z) THEN brother (Y, Z) Rule 2: IF father (X, Y) THEN payTuition (X, Y) Rule 3: IF brother (X, Y) THEN like (X, Y) Working Memory father (M.Tariq, Ali) father (M.Tariq, Ahmed) brother (Ali, Ahmed) payTuition (M.Tariq, Ali) payTuition (M.Tariq,Ahmed) like (Ali, Ahmed)

11. Expert System Example: Raining Knowledge Base Rule 1: IF person(X) AND person(Y) AND likes (X, Y) AND sameSchool(X,Y) THEN friends(X, Y) Rule 2: IF friends (X, Y) AND weekend() THEN goToMovies(X) goToMovies(Y) Rule 3: IF goToMovies(X) AND cloudy() THEN carryUmbrella(X) Working Memory person (Ali) person (Ahmed) cloudy () likes(Ali, Ahmed) sameSchool(Ali, Ahmed) weekend() friends(Ali, Ahmed) goToMovies(Ali) goToMovies(Ahmed) carryUmbrella(Ali) carryUmbrella(Ahmed)

12. Summary The main components of an ES are –Knowledge Base –Working Memory –Inference Engine