Rule-Based Expert Systems

1. Rule-Based Expert Systems CPS 4801

2. About the midterm exam • Exam on March 13 Tuesday (Tentatively) • Review on March 8 Thursday • Grades will be out by March 15, before spring break.

3. Strong AI vs. Weak AI • Strong AI is artificial intelligence that matches or exceeds human intelligence. • “Artificial general intelligence” • The weak AI hypothesis: machines can demonstrate intelligence, but do not necessarily have a mind, mental states or consciousness.

4. “General purpose” intelligence vs. Domain-specific intelligence • “General purpose” intelligence • Understand how the world works in general • requires vast amounts of knowledge about the world. • Domain-specific intelligence • Restricted to a particular domain • Knowledge is deep, but not wide. • Avoids the world knowledge problem, and is much more feasible for implementation.

5. Expert Systems • Domain expert: A person who has deep knowledge (in the form of facts and rules) and strong personal experience in a particular domain. • An expert system performs at a human expert level in a narrow and specialized domain.

6. Medical Diagnostics • simple expert system • http://familydoctor.org/familydoctor/en/health-tools/search-by-symptom.html

7. DXplain DXplainfirst launched in 1986 • Users enter clinical information, then ask DXplain to provide diagnostic decision support • DXplainknowledge base (KB) covers ~2400 diseasesand over 5000 clinical findings (signs, symptoms, epidemiologic data, laboratory findings, etc.) • Demo: http://dxplain.mgh.harvard.edu/dxp/dxp.sdemo.pl • Info: http://lcs.mgh.harvard.edu/projects/dxplain.html

8. GIDEON Global Infectious Disease and Epidemiology Network • Online application that supports the diagnosis and treatment of infectious diseases • Organized by country of origin • Updated weekly • Info: http://www.gideononline.com/

9. Characteristics of Expert Systems • Often a tradeoff between accuracy and speed. • Expert systems apply heuristics to guidethe reasoning process.

10. Reasoning + Knowledge + Facts • Human expertise typically breaks down into: • Ability to reason • Knowledgeabout the domain • Factsabout the particular situation (e.g. this patient’s symptoms) • Expert Systems use symbolic reasoning to solve problems • Symbols represent facts and rules (i.e. knowledge)

11. Characteristics of Expert Systems • Expert systems provide explanation facilities to display reasoning to users. • How did you come to that conclusion or diagnosis? • E.g., Why do you think I have a migraine? • Well, you have frequent, intense pain in the temple area, associated with nausea. Also, you aren’t taking any medications that are likely to produce these symptoms.

12. Characteristics of Expert Systems • Expert systems make mistakes • So do human experts! • Users have to be aware of this possibility

13. Rules • Production rules (most commonly used type) • IF the ‘traffic light’ is green • THEN the action is go • IF the ‘traffic light’ is red • THEN the action is stop

14. Rules • Any rule consists of two parts: the IF part, called the antecedent (premise or condition) and the THEN part called the consequent (conclusion or action). • IF <antecedent> • THEN <consequent> • Alternate syntax: • <antecedent>  <consequent>

15. Multiple antecedents • A rule can have multiple antecedents joined by the keywords AND (conjunction), OR (disjunction) or a combination of both. • IF animal is horse-shaped • AND animal has stripes • THEN animal is zebra • IF animal is hippo • OR animal is lion • THEN animal is dangerous

16. Multiple consequents • Multiple consequents are possible, and are connected by conjunctions. • IF tsunami alarm is sounding • AND date is not first Monday in month • THEN Condition is dangerous • ANDAdvice is “move away from the ocean”

17. Structure of antecedents and consequents • The antecedent of a rule incorporates two parts: an objectand its value. The object and its value are linked by an operator. • Operators • is, are, is not, are not are used to assign a symbolic value to a linguistic object. • mathematical operators to define an object as numerical and assign it to the numerical value. • IF ‘age of the customer’ < 18 • AND ‘cash withdrawal’ > 1000 • THEN ‘signature of the parent’ is required

18. Semantics of rules • Relation IF the ‘fuel tank’ is empty THEN the car is dead • Recommendation IF the season is autumn AND the sky is cloudy AND the forecast is drizzle THEN the advice is ‘take an umbrella’ • Directive IF the car is dead AND the ‘fuel tank’ is empty • THEN the action is ‘refuel the car’

19. Strategy IF the car is dead THEN the action is ‘check the fuel tank’; step1 is complete IF step1 is complete AND the ‘fuel tank’ is full THEN the action is ‘check the battery’; step2 is complete • Heuristic IF the spill is liquid AND the ‘spill pH’ < 6 AND the ‘spill smell’ is vinegar THEN the ‘spill material’ is ‘acetic acid’

20. So what are facts?

21. Rule-based expert system • An expert system whose knowledge base (KB) contains a set of production rules.

22. The Main Players In The Expert System Development Team

23. Structure of a rule-based expert system

24. Inference Engine • domain knowledge: IF-THEN rules • data: facts about the current situation • When the IF (condition) part of the rule matches a fact, the rule is fired. • The matching of the rule IF parts to the facts produces inference chains(new facts are discovered).

26. Inference engine algorithm • Inference engine compares each rulewith facts it already “knows” about,matching the antecedent (IF condition) • When the antecedent matches one ormore known facts, the rule fires andits consequent (THEN) is executed

27. Inference Chain • An inference chain indicates how an expert system applies rules to reach a conclusion given: A, B, D, E

28. Inference Chain • An inference chain indicates how an expert system applies rules to reach a conclusion given: A, B, D, E

29. Inference Chain • An inference chain indicates how an expert system applies rules to reach a conclusion given: A, B, D, E

30. Two approaches • Forward chaining • Backward chaining

31. Forward chaining (data-driven search) • Starts with known data (facts). • Firethe rules that have an antecedent that matches facts in the database, and add any resulting facts to the database. • Each rule can fire only once. • Each time only the topmost rule is executed. • When no more rules can fire, stop.

32. Forward Chaining

33. Forward Chaining Exercise 1 • Use forward chaining to prove the following:

34. Forward Chaining Exercise 1 • Facts: • Rules fired: • Use forward chaining to prove the following: A B C D E A B C D E X Y Q R S T Z A B C D E X A B C D E X Y A B C D E X Y Q A B C D E X Y Q R A B C D E X Y Q R S A B C D E X Y Q R S T • Proven: A  X A  Y A & D  Q Q  R R  S Q  T T  Z Z

35. Forward Chaining Exercise 2 • Use forward chaining to prove the following:

36. Forward Chaining Exercise 2 • Facts: • Rules fired: • Use forward chaining to prove the following: A B C D E X Y Q R S T Z • Cannot prove: • No more rules left to fire A  X A  Y A & D  Q Q  R R  S Q  T T  Z L

37. Forward Chaining +/- • Good for answering “What is the situation?” kind of questions (e.g. “What kind of animal is this?”) • Fires a lot of rules, and generates a lot of facts that might be irrelevant to the problem • If our goal is to infer only one particular fact, the forward chaining inference technique would not be efficient.

38. Backward chaining (goal-driven search) • System has hypothetical solution(s) (e.g. “The patient has type I diabetes”), and tries to prove it. • Find rules that consequents that match the goal. • If antecedents match the facts, stop. • If not, make the antecedents the new subgoals, and repeat.

39. Backward chaining algorithm • At the first iteration, rule(s) with thedesired goal in the consequent are selected • Stack up and attain many subgoals until.... • If the antecedent matches known data,the rule is fired and the goal is proven • Otherwise, if no rules remain,the desired goal is not proven

40. Backward chaining

41. Backward chaining

42. Backward chaining Exercise 1 • Use backward chaining to prove the following:

43. Backward chaining Exercise 1 • Facts: • Stack of rules: (subgoals) • Rules fired: • Use backward chaining to prove the following: A B C D E Q T Z A B C D E • Proven: A & D  Q Q  T T  Z Z A & D  Q Q  T T  Z

44. Backward chaining Exercise 2 • Use backward chaining to prove the following:

45. Backward chaining Exercise 2 • Facts: • Stack of rules: (subgoals) • Cannot prove: • Subgoal N cannot be proven • Use backward chaining to prove the following: A B C D E N  L L

46. Backward chaining +/- • Efficient way to prove or disprove a particular hypothesis. • Sometimes more efficient with a small set of hypotheses • Less efficient than forward chaining if large number of hypotheses

47. Forward vs. backward chaining • If an expert first needs to gather some information and then tries to infer from it whatever can be inferred, choose the forward chaining inference engine. • However, if your expert begins with a hypothetical solution and then attempts to find facts to prove it, choose the backward chaining inference engine.

48. Forward vs. backward chaining • Forward chaining: best for analysis and interpretation (e.g. DENDRAL (1971) determines molecular structure of soil sample). • Backward chaining: best for diagnosis (e.g. MYCIN (1976) diagnoses infectious blood diseases).

49. Forward + backward chaining • Most real expert systems use both. • Primary inference is backward chaining. • Switches to forward chaining when new data is added. • Minimizes pointless queries to user (backward chaining), but exploits any facts that are acquired.

50. Conflict resolution • Rules with identical antecedents (IF conditions) can cause conflicts via their consequents (THEN clauses) • Rule 1: IF the ‘traffic light’ is green THEN the action is go • Rule 2: IF the ‘traffic light’ is red THEN the action is stop • Rule 3: IF the ‘traffic light’ is red THEN the action is go