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Chapter 9 Rules and Expert Systems

Search Methods. Reasoning/ Proof. Problem Solving. Predicate Logic. Pattern Recognition Data Mining, etc. Chapter 9 Rules and Expert Systems. Bayesian Network. Probability (statistics). Expert System (Rule Based Inference). Uncertainty (cybernetics). Machine Learning

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Chapter 9 Rules and Expert Systems

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  1. Search Methods Reasoning/ Proof Problem Solving Predicate Logic Pattern Recognition Data Mining, etc. Chapter 9 Rules and Expert Systems Bayesian Network Probability (statistics) Expert System (Rule Based Inference) Uncertainty (cybernetics) Machine Learning Fuzzy, NN, GA, etc. • Diagnosis (Decision Making) • Advice (Recommendations) • Automatic Control Fuzzy Theorem

  2. Recall: Deduction over FOPC --- Resolution Barks_at(Snoopy,John) False Dog(X) ^ Meets(X,Y)^Dislikes(X,Y)  Barks_at(X,Y) {X/Snoopy, Y/John} Dog(Snoopy) ^ Meets(Snoopy,John)^Dislikes(Snoopy,John)  F T  Dog(Snoopy) Meets(Snoopy,John)^Dislikes(Snoopy,John)  False Close_to(Z,DormG)  Meets(Snoopy, Z) {Z/John} Close_to(John,DormG)^Dislikes(Snoopy,John)  False True  Close_to(John, DormG) Dislikes(Snoopy,John)  False Man(W)  Dislikes(Snoopy,W) {W/John} Man(John)  False True  Man(John) True  False 此為 “Implies” Normal Form 了解此法可以銜接 “Expert System”

  3. Recall: Refutation Proofs in Tree Form Forward Chaining + Refutation P, PQ Should Q be true? PQ TrueP QFalse ¬P VQ P ¬Q TrueQ Q  這兩種形式  皆有相關研究採用 TrueFalse ^ Backward Chaining (Resolution) QFalse PQ TrueP ¬Q ¬P VQ P PFalse ¬P TrueFalse ^

  4. Chapter 9 Contents • Rules for Knowledge Representation • Rule Based Production Systems • Forward Chaining • Conflict Resolution • Meta Rules • Backward Chaining • The Architecture of Expert Systems • Expert System Shells • The Rete Algorithm • Knowledge Engineering • CLIPS: C Language Integrated Production System • Backward Chaining in Expert Systems • CYC System

  5. Rules for Knowledge Representation • IF… THEN Rules can be used to represent knowledge: • IF it rains, then you will get wet • Rules can also be recommendations: • IF it rains, then you should wear a coat

  6. Example (Rule Based Inference) rains_on(x)  wet(x) Truerains_on(John) {x/John} Truewet(John) wet(John)False …wet(John)是否成立? ¬ wet(John)False … ¬wet(John)如何能成立? p.s. 不穿雨衣一定溼，但穿雨衣不保證不溼 rains_on(x) ^ ¬coat(x)  wet(x) Truerains_on(John) {x/John} Trure  ¬coat(John) ¬coat(John)  wet(John) Truewet(John) 如果¬coat(John) 會導致 wet(John) 成立

  7. 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 a database of rules (knowledge base), a database of facts, and an inference engine which reasons about the facts using the rules.

  8. Forward Chaining • 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 fires, and the conclusion of the rule is added to the database of facts.

  9. Example (Forward Chaining) Man(W)  Dislikes(Snoopy, W) Close_to(Z,DormG)  Meets(Snoopy, Z) TMan(John) TClose_to(John, DormG) {Z/John} {W/John} TDislikes(Snoopy, John) (1) (2) TMeets(Snoopy, John) (3) Dog(X) ^ Meets(X,Y)^Dislikes(X,Y)  Barks_at(X,Y) TDog(Snoopy) (4) Meets(Snoopy,Y)^Dislikes(Snoopy,Y)  Barks_at(Snoopy,Y) {Y/John} (5) Meets(Snoopy,John)  Barks_at(Snoopy,John) ? Barks_at(Snoopy,John) False TBarks_at(Snoopy,John)

  10. Conflict Resolution牴觸的規則 • 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. (1) 天氣好,睡得好,工作不忙 去打球； vs.“吃飽飯不打球” (2) 男生,穿拖鞋,靠近女生宿舍小花吠; vs.“吃飯中不吠人” Priority?

  11. 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. • Meta rules are treated by the system like normal rules, but are given higher priority.

  12. 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.

  13. Example (Backward Chaining) Barks_at(Snoopy,John) False Dog(X) ^ Meets(X,Y)^Dislikes(X,Y)  Barks_at(X,Y) Man(W)  Dislikes(Snoopy, W) {X/Snoopy, Y/John} Dog(Snoopy) ^ Meets(Snoopy,John)^Dislikes(Snoopy,John)  F T  Dog(Snoopy) Meets(Snoopy,John)^Dislikes(Snoopy,John)  False Close_to(Z,DormG)  Meets(Snoopy, Z) {Z/John} Close_to(John,DormG)^Dislikes(Snoopy,John)  False True  Close_to(John, DormG) Dislikes(Snoopy,John)  False {W/John} Man(John)  False True  Man(John) True  False

  14. Recall: Example (Forward Chaining) Man(W)  Dislikes(Snoopy, W) Close_to(Z,DormG)  Meets(Snoopy, Z) TMan(John) TClose_to(John, DormG) {Z/John} {W/John} TDislikes(Snoopy, John) TMeets(Snoopy, John) Dog(X) ^ Meets(X,Y)^Dislikes(X,Y)  Barks_at(X,Y) TDog(Snoopy) Meets(Snoopy,Y)^Dislikes(Snoopy,Y)  Barks_at(Snoopy,Y) {Y/John} Meets(Snoopy,John)  Barks_at(Snoopy,John) TBarks_at(Snoopy,John)

  15. Chapter 9 Contents • Rules for Knowledge Representation • Rule Based Production Systems • Forward Chaining • Conflict Resolution • Meta Rules • Backward Chaining • The Architecture of Expert Systems • Expert System Shells • The Rete Algorithm • Knowledge Engineering • CLIPS: C Language Integrated Production System • Backward Chaining in Expert Systems • CYC System

  16. 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.

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

  18. Expert System Shells • The part of an expert system(內容不算) • Neitherdomain specific nor case specific knowledge is contained in 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.

  19. The Rete Algorithm root • A rete is a directed, acyclic, rooted graph (i.e. a “Search 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 from the root to the leaves. • This makes an efficient way for expert systems to deal with environments which change often. man(J) ¬man(J) dog(S) ¬dog(S) recommendations

  20. Knowledge Engineering • A knowledge engineer takes knowledge from experts and inputs it into the expert system. (擷取) • A knowledge engineer will usually choose which expert system shell to use. (實現) • The knowledge engineer is also responsible for entering meta-rules. (整合) Expert System (Rule Based Production System) 只是後來所謂「知識工程」的一個小部份

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

  22. 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. 相輔相成

  23. 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.

  24. Relevant Research Search Methods Reasoning/ Proof Problem Solving Predicate Logic Pattern Recognition Data Mining, etc. Bayesian Network Probability (statistics) Expert System (Rule Based Inference) Uncertainty (cybernetics) Machine Learning Fuzzy, NN, GA, etc. • Diagnosis (Decision Making) • Advice (Recommendations) • Automatic Control Fuzzy Theorem In my opinion: weak sub? strong 記憶 評判 搜尋解答 邏輯推理 控制 學習 創作 Fuzzy,NN,GA

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