Chapter 9 Rules and Expert Systems

1. Chapter 9 Rules and Expert Systems

2. Definition of Expert System • An expert system is a computerprogram that represents and reasons with knowledge of some specialist subject with a view to solve problemsor giving advice.

3. Chapter 9 Contents (1) • Rules for Knowledge Representation • Rule Based Production Systems • Forward Chaining • Conflict Resolution • Meta Rules • Backward Chaining • The Architecture of Expert Systems • Expert System Shells

4. Chapter 9 Contents (2) • The Rete Algorithm • Knowledge Engineering • CLIPS • Backward Chaining in Expert Systems • CYC

5. Rules for Knowledge Representation • IF… THEN Rules can be used to represent knowledge: • IF it rains, then you will get wet • IF A THEN B Can be expressed as: A B • Here, A is called antecedent, and B is called consequent. • Rules can also be recommendations: • IF it rains, then you should wear a coat

6. Rule Based Production Systems • A production system is a system that uses knowledge in the form of rules to provide diagnoses or advice on the basis of input data. • The system consists of: • 1. A database of rules (knowledge base), • 2. A database of facts, and • 3. An inference engine which reasons about the facts using the rules.

7. Forward Chaining(Data Driven System) • Forward chaining is a reasoning model that works from a set of facts and rules towards a set of conclusions, diagnoses or recommendations. • When a fact matches the antecedent of a rule, the rule is triggered, then the rule is fired, and the conclusion of the rule is added to the database of facts.

8. Example • Consider the rules that is used to control an elevator in a 3-story building: • Rule 1: • IF on the first floor and button is pressed • on first floor • THEN open door • Rule 2: • IF on first floor • AND button is pressed on second floor • THEN goto second floor

9. Example • Rule 3: • IF on first floor • AND button is pressed on third floor • THEN go to third floor • Rule 4: • IF on second floor • AND button is pressed on first floor • AND already going to third floor • THEN remember to go to first floor later … , …

10. The example only listed a subset of the rules (partial rules), we can keep doing the list of the rules to finish the control. • How do you finish the list?

11. Conflict Resolution • Sometimes more than one rule will fire at once, and a conflict resolution strategy must be used to decide which conclusions to use. • One strategy is to give rules priorities and to use the conclusion that has the highest priority. • Other strategies include applying the rule with the longest antecedent, or applying the rule that was most recently added to the database.

12. Example • Consider the following rules: • IF it is cold • THEN wear a coat • IF it is cold • THEN stay at home • IF it is cold • THEN turn on the heat • Obviously, one antecedent can trigger three rules here. We might need to give the rules priorities to decide which one to pick first. • Or, we can add more conditions to make some antecedents longer than others.

13. Example • By applying priorities: • IF patient has pain • THEN prescribe painkillers priority 10 • … • IF patient has chest pain • THEN treat for heart disease priority 100

14. Make Differences in Antecedents According Their Length • IF patient has pain • THEN prescribe painkiller • … • IF patient has chest pain • AND patient is over 60 • AND patient has history of heart conditions • THEN take to emergency room

15. Meta Rules • The rules that determine the conflict resolution strategy are called meta rules. • Meta rules define knowledge about how the system will work. • For example, meta rules might define that knowledge from Expert A is to be trusted more than knowledge from Expert B. • This kind of knowledge is called meta knowledge. • It is knowledge about knowledge. • Meta rules are treated by the system like normal rules, but are given higher priority.

16. Backward Chaining • In cases where a particular conclusion is to be proved, backward chaining can be more appropriate. • Works back from a conclusion towards the original facts. • When a conclusion matches the conclusion of a rule in the database, the antecedents of the rule are compared with facts in the database.

17. Comparing Forward and Backward Chaining • Choose forward or backward chaining will depend on the specific problems. • Sometimes backward chaining is much faster and sometimes forward chaining is much faster. • Sometimes, they are about the same.

18. Example • Rules: • 1. A Λ B C • 2. A B • 3. C Λ D E • 4. B Λ E Λ F G • 5. A Λ E H • 6. D Λ E Λ H I • Facts: 1. A 2. B 3. F • Goal: to prove H.

19. By using Forward Chaining • Facts Rules Triggered Rule Fired • A, B, F 1, 2 1 • A, B, C, F 2 2 • A, B, C, D, F 3 3 • A, B, C, D, E, F 4, 5 4 • A, B, C, D, E, F, G 5 5 • A, B, C, D, E, F, G, H 6 Stop

20. Backward Chaining • Facts Goal Matching Rules • A, B, F H 5 • A, B, F E 3 • A, B, F C, D 1 • A, B, C, F D 2 • A, B, C, D, F Stop • This specific example has made us use 1 rule less than forward chaining.

21. The Architecture of Expert Systems (1) • An expert system uses expert knowledge derived from human experts to diagnose illnesses, provide recommendations and solve other problems.

22. The Architecture of Expert Systems (2) • Knowledge base: database of rules (domain knowledge). • Explanation system: explains the decisions the system makes. • User Interface: the means by which the user interacts with the expert system. • Knowledge base editor: allows the user to edit the information in the knowledge base.

23. Expert System Shells • The part of an expert system that does not contain any domain specific or case specific knowledge is the expert system shell. • A single expert system shell can be used to build a number of different expert systems. • An example of an expert system shell is CLIPS.

24. The Rete Algorithm • A rete is a directed, acyclic, rooted graph (a tree). • A path from the root node to a leaf represents the left hand side of a rule. • Each node stores details of which facts have been matched so far. • As facts are changed, the changes are propagated through the tree. • This makes an efficient way for expert systems to deal with environments which change often.

25. Knowledge Engineering • A knowledge engineer takes knowledge from experts and inputs it into the expert system. • A knowledge engineer will usually choose which expert system shell to use. • The knowledge engineer is also responsible for entering meta-rules.

26. CLIPS • CLIPS is C Language Integrated Production System – an expert system shell. • CLIPS uses a LISP-like notation to enter rules.

27. Backward Chaining in Expert Systems • Backward chaining is often used in expert systems that are designed for medical diagnosis: • For each hypothesis, H: • If H is in the facts database, it is proved. • Otherwise, if H can be determined by asking a question, then enter the user’s answer in the facts database. Hence, it can be determined whether H is true or false, according to the user’s answer. • Otherwise, find a rule whose conclusion is H. Now apply this algorithm to try to prove this rule’s antecedents. • If none of the above applies, we have failed to prove H. • Usually backward chaining is used in conjunction with forward chaining.

28. CYC • A frame based production system. • Uses a database of over 1,000,000 facts and rules, encompassing all fields of human knowledge. • CYC can answer questions about all kinds of knowledge in its database, and can even understand analogies, and other complex relations.

29. What is CYC? • CYC is the name of a very large, multi-contextual knowledge base and inference engine, the development of which started at the Microelectronics and Computer Technology Corporation (MCC) in Austin, Texas during the early 1980s.

30. CYC • Over the past eleven years the members of the CYC team have added to the knowledge base a huge amount of fundamental human knowledge: facts, rules of thumb, and heuristics for reasoning about the objects and events of modern everyday life.

31. CYC • CYC is an attempt to do symbolic AI on a massive scale. • It is not based on numerical methods such as statistical probabilities, nor is it based on neural networks or fuzzy logic. • All of the knowledge in CYC is represented declaratively in the form of logical assertions. • CYC presently contains approximately 400,000 significant assertions, which include simple statements of fact, rules about what conclusions to draw if certain statements of fact are satisfied (true), and rules about how to reason with certain types of facts and rules. • New conclusions are derived by the inference engine using deductive reasoning.

32. CYC • Who is doing CYC? • Much of the CYC work has been done at the Microelectronics and Computer Technology Corporation in Austin, Texas. • As of the First of January in 1995, a new independent company named Cycorp has been created to further the work done on the CYC project. • Cycorp continues to be based in Austin, Texas. • The development of CYC has been supported by several organizations, including Apple, Bellcore, DEC, DoD, Interval, Kodak, and Microsoft.