Artificial Intelligence

1. Artificial Intelligence Knowledge Representation Problem 2

2. first-order logic • for all x: (NOT(Knows(John, x)) OR IsMean(x) OR Loves(John, x)) • John loves everything he knows, with the possible exception of mean things • for all y: (Loves(Jane, y) OR Knows(y, Jane)) • Jane loves everything that does not know her

3. Converting sentences to CNF 1. Eliminate all ↔ connectives (P ↔ Q)  ((P  Q) ^ (Q  P)) 2. Eliminate all  connectives (P  Q)  (P  Q) 3. Reduce the scope of each negation symbol to a single predicate P  P (P  Q) P Q (P  Q) P Q (x)P  (x)P (x)P  (x)P 4. Standardize variables: rename all variables so that each quantifier has its own unique variable name

4. Converting sentences 5. Eliminate existential quantification by introducing Skolem constants/functions (x)P(x)  P(c) c is a Skolem constant (a brand-new constant symbol that is not used in any other sentence) (x)(y)P(x,y)  (x)P(x, f(x)) since  is within the scope of a universally quantified variable, use a Skolem function f to construct a new value that depends on the universally quantified variable f must be a brand-new function name not occurring in any other sentence in the KB. E.g., (x)(y)loves(x,y)  (x)loves(x,f(x)) In this case, f(x) specifies the person that x loves

5. Generalized Modus Ponens

6. Modus Ponens - special case of Resolution • p Þ q • p • q • Sunday Þ Dr Yasser is teaching AI • Sunday • Dr Yasser teaching AI • Using the tricks: • p Þ q • Þ p • p Þ p Ù q • q, i.e. q

7. Sound rules of inference • Each can be shown to be sound using a truth table RULEPREMISE CONCLUSION Modus Ponens A, A  B B And Introduction A, B A  B And Elimination A  B A Double Negation A A Unit Resolution A  B, B A Resolution A  B, B  C A  C

8. An example (x)(P(x)  ((y)(P(y)  P(f(x,y))) (y)(Q(x,y)  P(y)))) 2. Eliminate  (x)(P(x)  ((y)(P(y)  P(f(x,y))) (y)(Q(x,y)  P(y)))) 3. Reduce scope of negation (x)(P(x)  ((y)(P(y)  P(f(x,y))) (y)(Q(x,y) P(y)))) 4. Standardize variables (x)(P(x)  ((y)(P(y)  P(f(x,y))) (z)(Q(x,z) P(z)))) 5. Eliminate existential quantification (x)(P(x) ((y)(P(y)  P(f(x,y))) (Q(x,g(x)) P(g(x))))) 6. Drop universal quantification symbols (P(x)  ((P(y)  P(f(x,y))) (Q(x,g(x)) P(g(x)))))

9. Two broad kinds of rule system • forward chaining systems, and backward chaining systems. • In a forward chaining system you start with the initial facts, and keep using the rules to draw new conclusions (or take certain actions) given those facts • In a backward chaining system you start with some hypothesis (or goal) you are trying to prove, and keep looking for rules that would allow you to conclude that hypothesis, perhaps setting new subgoals to prove as you go.

10. Forward chaining • Proofs start with the given axioms/premises in KB, deriving new sentences until the goal/query sentence is derived • This defines a forward-chaining inference procedure because it moves “forward” from the KB to the goal [eventually]

11. Forward chaining • Idea: fire any rule whose premises are satisfied in the KB, • add its conclusion to the KB, until query is found

12. Forward chaining example

13. Backward chaining • Proofs start with the goal query, find rules with that conclusion, and then prove each of the antecedents in the implication • Keep going until you reach premises • Avoid loops: check if new sub-goal is already on the goal stack • Avoid repeated work: check if new sub-goal • Has already been proved true • Has already failed

14. Backward Chaining Is Tom faster than someone?

15. Forward chaining example • KB: • allergies(X)  sneeze(X) • cat(Y)  allergic-to-cats(X)  allergies(X) • cat(Felix) • allergic-to-cats(Lise) • Goal: • sneeze(Lise)

16. Exercise • You go to the doctor and for insurance reasons they perform a test for a horrible disease • You test positive • The doctor says the test is 99% accurate • Do you worry?

17. Reduction to propositional inference Suppose the KB contains just the following: x King(x)  Greedy(x)  Evil(x) King(Ali) Greedy(Ali) Brother(Saad, Ali) Instantiating the universal sentence in all possible ways, we have: King(John)  Greedy(John)  Evil(John) King(Richard)  Greedy(Richard)  Evil(Richard) King(John) Greedy(John) Brother(Richard,John) • The new KB is propositionalized: proposition symbols are King(John), Greedy(John), Evil(John), King(Richard),etc.

18. An example • Sameh is a lawyer. • Lawyers are rich. • Rich people have big houses. • Big houses are a lot of work. • We would like to conclude that Sameh’s house is a lot of work.

19. Axiomatization 1 • lawyer(Sameh) • x lawyer(x)  rich(x) • x rich(x)  y house(x,y) • x,y rich(x)  house(x,y)  big(y) • x,y ( house(x,y)  big(y)  work(y) ) • 3 and 4, say that rich people do have at least one house and all their houses are big. • Conclusion we want to show: house(Sameh, S_house)  work(Sameh, S_house) • Or, do we want to conclude that Sameh has at least one house that needs a lot of work? I.e. • y house(Sameh,y)  work(y)

20. Hassan and the cat • Every animal owner is an animal lover • Everyone who loves all animals is loved by someone. • Anyone who kills an animal is loved by no one. • Mustafa owns a dog. • Either Mustafa or Hassan killed the cat, who is named SoSo. • Did Hassan kill the cat?

21. Practice exampleDid Hassan kill the cat • Mustafa owns a dog. Every dog owner is an animal lover. No animal lover kills an animal. Either Hassan or Mustafa killed the cat, who is named SoSo . Did Hassan kill the cat? • These can be represented as follows: A. (x) Dog(x)  Owns(Mustafa ,x) B. (x) ((y) Dog(y)  Owns(x, y))  AnimalLover(x) C. (x) AnimalLover(x)  ((y) Animal(y) Kills(x,y)) D. Kills(Mustafa ,SoSo)  Kills(Hassan,SoSo) E. Cat(SoSo) F. (x) Cat(x)  Animal(x) G. Kills(Hassan, SoSo) GOAL

22. Convert to clause form A1. (Dog(D)) A2. (Owns(Mustafa,D)) B. (Dog(y), Owns(x, y), AnimalLover(x)) C. (AnimalLover(a), Animal(b), Kills(a,b)) D. (Kills(Mustafa,SoSo), Kills(Hassan,SoSo)) E. Cat(SoSo) F. (Cat(z), Animal(z)) • Add the negation of query: G: (Kills(Hassan, SoSo))

23. The resolution refutation proof R1: G, D, {} (Kills(Mustafa,SoSo)) R2: R1, C, {a/Mustafa, b/SoSo} (~AnimalLover(Mustafa), ~Animal(SoSo)) R3: R2, B, {x/Mustafa} (~Dog(y), ~Owns(Mustafa, y), ~Animal(SoSo)) R4: R3, A1, {y/D} (~Owns(Mustafa, D), ~Animal(SoSo)) R5: R4, A2, {} (~Animal(SoSo)) R6: R5, F, {z/SoSo} (~Cat(SoSo)) R7: R6, E, {} FALSE

24. D G {} R1: K(J,T) C {a/J,b/T} • The proof tree B R2: AL(J)  A(T) {x/J} R3: D(y)  O(J,y)  A(T) A1 {y/D} R4: O(J,D), A(T) A2 {} R5: A(T) F {z/T} R6: C(T) A {} R7: FALSE

25. Example knowledge base • The law says that it is a crime for an American to sell weapons to hostile nations. The country Nono, an enemy of America, has some missiles, and all of its missiles were sold to it by Colonel West, who is American. • Prove that Col. West is a criminal

26. Example knowledge base ... it is a crime for an American to sell weapons to hostile nations: American(x)  Weapon(y)  Sells(x,y,z)  Hostile(z)  Criminal(x) Nono … has some missiles, i.e., x Owns(Nono,x)  Missile(x): Owns(Nono,M1) and Missile(M1) … all of its missiles were sold to it by Colonel West Missile(x)  Owns(Nono,x)  Sells(West,x,Nono) Missiles are weapons: Missile(x)  Weapon(x) An enemy of America counts as "hostile“: Enemy(x,America)  Hostile(x) West, who is American … American(West) The country Nono, an enemy of America … Enemy(Nono,America)

27. Resolution proof: definite clauses 

28. Rule-Based Systems • Also known as “production systems” or “expert systems” • Rule-based systems are one of the most successful AI paradigms • Used for synthesis (construction) type systems • Also used for analysis (diagnostic or classification) type systems

29. Rule-Based Systems • Instead of representing knowledge in a relatively declarative, static way (as a bunch of things that are true), rule-based system represent knowledge in terms of a bunch of rules that tell you what you should do or what you could conclude in different situations. • A rule-based system consists of a bunch of IF-THEN rules, a bunch of facts, and some interpreter controlling the application of the rules, given the facts.

30. IF (lecturing X) AND (marking-practicals X) THEN ADD (overworked X) • IF (month february) THEN ADD (lecturing ali) • IF (month february) THEN ADD (marking-practicals ali) • IF (overworked X) OR (slept-badly X) THEN ADD (bad-mood X) • IF (bad-mood X) THEN DELETE (happy X) • IF (lecturing X) THEN DELETE (researching X)

31. Rule Based Reasoning • The advantages of rule-based approach: • The ability to use • Good performance • Good explanation • The disadvantage are • Cannot handle missing information • Knowledge tends to be very task dependent

32. Other Reasoning • There exist some other approaches as: • Case-Based Reasoning • Model-Based Reasoning • Hybrid Reasoning • Rule-based + case-based • Rule-based + model-based • Model-based + case-based

33. Expert System • An Expert System is a computer program that represents and reasons with knowledge of some specialist subject with a view to solving problems or giving advice • It is practical program that use heuristic strategies developed by humans to solve specific class of problems

34. Expert System Functionality • replace human expert decision making when not available • assist human expert when integrating various decisions • provides an ES user with • an appropriate hypothesis • methodology for knowledge storage and reuse • expert system – software systems simulating expert-like decision making while keeping knowledge separate from the reasoning mechanism

35. Parties in XS world • Human ExpertCan solve problems; we desire to solve the problems without her. • Knowledge EngineerCan communicate with HE to obtain and model the knowledge that we need in the system • ProgrammerBuilds and maintains all the necessary computer programs • UserWants to use expertise to solve problems (better, cheaper)

36. Expert System User Interface Question&Answer Natural Language Graphical interface Knowledge editor User General Knowledge Inference Engine Case-specific data Explanation

37. Expert System Components • Global Database • content of working memory (WM) • Production Rules • knowledge-base for the system • Inference Engine • rule interpreter and control subsystem

38. Rule-Based System • knowledge in the form of if condition then effect (production) rules • reasoning algorithm: (i) FR  detect(WM) (ii) R  select(FR) (iii) WM  apply R (iv) goto (i) • conflicts in FR: • examples – CLIPS (OPS/5), Prolog

39. The Structure of ES INFERENCE ENGINE • Knowledge processor which is modeled after the expert reasoning power. • 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 conclusion about the problems. • It taps the knowledge base and working memory to derive new information and solve problems

40. The Structure of ES THE USER INTERFACE • The user communicates with the expert system through the user interface. • It allows the user to query the system, supply information and receive advice. • The aims are to provide the same form of communication facilities provided by the expert. • But normally has less capability of understanding natural language and general knowledge.

41. The Structure of ES THE EXPLANATION FACILITY • A trademark of expert systems: ability to explain their reasoning. • An additional component of expert system. • ES can provide explanation on: • WHY it is asking the question • HOW it reached some conclusion.

42. Main challenges in Expert Systems field • Acquiring knowledgeExpert is unaware, uncommunicative, busy, unwilling • Representing knowledgeFacts, Relations, Conclusions, Meta-knowledge • Controlling reasoningSelection between alternatives is guided by higher order knowledge (meta rules) • Explanation • Sequence of reasoning steps? • Interpretation at higher level • Why were other steps NOT chosen? • Quality evaluation; acceptance

43. Weaknesses of Expert Systems • Require a lot of detailed knowledge • Restrict knowledge domain • Not all domain knowledge fits rule format • Expert consensus must exist • Knowledge acquisition is time consuming • Truth maintenance is hard to maintain • Forgetting bad facts is hard

44. Expert Systems in Practice • XCON/R1 • classical rule-based system • configuration DEC computer systems • commercial application, well used, followed by XSEL, XSITE • failed operating after 1700 rules in the knowledge base • FelExpert • rule-based, baysian model, • taxonomised, used in a number of applications • ICON • configuration expert system • uses proof planning structure of methods

45. Selected Business Expert Systems and Functions

46. Selected Business Expert Systems and Functions

47. Selected Business Expert Systems and Functions

48. MYCIN: A medical expert system • Developed at Stanford University in the mid 1970's. • The first large expert system that perform at the level of human expert and use as benchmark by expert system developers. • Provide consultative advise about bacteremia and meningitis. • Bacteremia is an infections that involve bacteria in the blood.

49. MYCIN: A medical expert system • Meningitis is an infection which is an inflammation of the membranes that envelop the brain and spinal cord. • Can be fatal, thus need quick response, but positive identification normally takes 24 - 48 hours. • Normally doctors must begin treatment in the absence of lab results. • Very complex and doctor need advice of an expert of bacteremia and meningitis.

50. How MYCIN reasons? • Laboratory results of body fluid analyses • Symptoms that patient is displaying • Characteristics of the patient such as age, sex etc.