1. UBI517 Expert Systems 1 Lecture 2 Knowledge Representation

2. UBI 517 Expert Systems 2 Knowledge representation Epistemology or theory of knowledge is the branch of philosophy that studies the nature, methods, limitations, and validity of knowledge and belief.philosophy knowledgebelief In other words, epistemology primarily addresses the following questions: "What is knowledge?", "How is knowledge acquired?", and "What do people know?" Wikipedia

3. UBI 517 Expert Systems 3 Knowledge representation Epistemology Addresses The structure used to describe the elements of knowledge. The interpretive process required to use the described knowledge.

4. UBI 517 Expert Systems 4 Representation Criteria Transparency Explicitness Naturalness Efficiency Adequacy Modularity

5. UBI 517 Expert Systems 5 Knowledge Representation Schemes Mypoulos and Lavesque (1984) classification: 1) Logical representation schemes First order predicate logic for knowledge representation (scheme) Prolog for implementing (media) 2) Procedural representation schemes Production systems

6. UBI 517 Expert Systems 6 Knowledge Representation Schemes 3) Network representation schemes Capture knowledge as a graph in which the nodes represent the objects or concepts in the problem domain and the arcs represent the relations or associations between them. Semantic networks Conceptual graphs 4) Structured representation schemes Each node is considered as a complex data structure consisting of named slots with attached values Frame-based systems

7. UBI 517 Expert Systems 7 Formal Logic Logic provides A representation of knowledge & Automation of the inferencing process Formal Logic Propositional Logic Predicate Logic

8. UBI 517 Expert Systems 8 Propositional Logic One of the oldest & simplest type of formal logic Formal indicates logic is concerned with form of logical statements as opposed to meaning

9. UBI 517 Expert Systems 9 Propositional Logic Propositional symbols denote propositions or statements about the world that may be either true or false. Examples It is raining. raining It is sunny. sunny If it is raining then it is not sunny. raining  sunny

10. UBI 517 Expert Systems 10 Propositional Logic The truth table for the implication connective is shown below.

11. UBI 517 Expert Systems 11 Propositional Logic For propositional expressions P, Q, and R:   P) = P  (P  Q) = (  P  Q)  De Morgan's Law:  (P  Q) = (  P  Q)  De Morgan's Law:  (P  Q) = (  P  Q)  distributive law: P  Q  R) = (P  Q)  P  R)  distributive law: P  Q  R) = (P  Q)  P  R)  commutative law: (P  Q) = (Q  P)  commutative law: (P  Q) = (Q  P)  associative law: P  Q  R) = (P  Q)  R  associative law: P  Q  R) = (P  Q)  R  contrapositive law: (P  Q) = (  Q  P)

12. UBI 517 Expert Systems 12 Propositional Logic PREMISE: All X are Y PREMISE: Z is a X Conclusion: Z is a Y Separating form from meaning is what gives logic its power as a tool Propositional logic is a symbolic logic for manipulating propositions or logical variables Propositions are classified as either T or F. KBS course is brilliant - (definitely T) Fish is good to eat - ( uncertain truth as depends on person) Doesn’t matter if X is apples, planes or Ships. Only the form is important.

13. UBI 517 Expert Systems 13 Propositional Logic He is tall - (variable involved so can’t assign value. Also tall is hard concept to define. Can’t be done in pred. Calculus). Proposition logic: assign a truth value to statements Propositional logic provides a mechanism for assigning a truth value to compound proposition based on value of individual propositions and connective involved Propositional logic connectives ConjunctionAND DisjunctionOR NegationNOT A’ Material implication If-Then --> Material equivalenceEquals

14. UBI 517 Expert Systems 14 Propositional Logic Prop Logic can only deal with complete sentences that is, it can not examine the internal structure of a statement. too simple for complex domains no support for inferencing doesn’t handle fuzzy concepts Solution - Predicate Logic

15. UBI 517 Expert Systems 15 First order predicate logic Predicate logic was developed in order to analyze more general cases. Propositional logic is a subset of predicate logic. Concerned with internal structure of sentences Quantifiers - all, some, no - make sentence more exact. Wider scope of expression.

16. UBI 517 Expert Systems 16 First order predicate logic an alphabet a formal language axioms inference rules

17. UBI 517 Expert Systems 17 an alphabet predicates represent the relations within the domain: man(tom) variables - Dog, Color functions - father(CHILD), plus (2, 3) constants - rover(a_dog), blue(a_color) connectives - and, or, not, is equivalent to.. quantifiers -  X,  X :  x [ PERSON(X)  NEED-AIR(X)] delimiters

18. UBI 517 Expert Systems 18 Language A legally constructed formula in the language is called a well-formed formula (wff) Evaluation is done by the use of truth tables.

19. UBI 517 Expert Systems 19 Language Knowledge engineering When Using Formal Logic Develop an understanding of the knowledge. Formulate the knowledge as English statements. Break the statements into their component parts. Choose symbols to represent the elements and relations in each component. Build wffs by using the above symbols that represent the statement. Predicate relation is derived by its name and its arity.

20. UBI 517 Expert Systems 20 Language Example: "Rover is a black dog“ "You will gain weight unless you exercise.“ “If the student’s average grade is greater than 90%, then the student will get an A in the course.” black(rover)  dog(rover)  X [¬exercise(X)  gain_weight(X) ]  X,  Name [(student(Name)  avrg_grade(Name, X)  ge(X, 90)  final_grade(Name, “A”) ]

21. UBI 517 Expert Systems 21 Inference Process Inference in formal logic is the process of generating new wffs from existing wffs through the application of rules of inference.

22. UBI 517 Expert Systems 22 Inference Process Conclusion: Many English sentences are ambiguous. There is often a choice of ways of representing the knowledge. Even in very simple situations a set of sentences is unlikely to contain all the information necessary to reason about the topic at hand.

23. UBI 517 Expert Systems 23 Inference Process Logical Inferences Modus ponens and modus tolens provide the foundation for making references. Modus ponens: ((p  q)  p)  q If someone is snorkeling then he is wet  x snorkeling(X)  wet(X) If we are given that alex is snorkeling snorkeling (alex) we can infer wet(alex)

24. UBI 517 Expert Systems 24 Inference Process Logical Inferences Modus tolens: ((p  q)  q)  p If someone is snorkeling then he is wet  x snorkeling(X)  wet(X) If we are given that alex is not wet  wet(alex) we can infer  snorkeling(alex)

25. UBI 517 Expert Systems 25 Inference Process There are three reasoning methods that can be applied to a set of premises. 1. Deduction 2. Abduction 3. Induction

26. UBI 517 Expert Systems 26 Inference Process Deduction is reasoning from known (premises) to unknown (logical conclusions).  x,  y,  z larger(x, y)  larger(y, z)  larger(x, z) If our list of axioms contain the axioms larger(house, car) larger(car, cat) Through deductive reasoning the wff larger(house, cat) Can be derived and added to our list of axioms

27. UBI 517 Expert Systems 27 Inference Process In Abduction we begin with a conclusion and procede to derive conditions that would make the conclusion valid. In other words we try to find an explanation for the conclusion It does not guarantee that a true conclusion results. Therefore it s called unsound rule of inference. If given   B and B is true Abduction allows us to say A is possibly true. Reasoning under uncertainty.

28. UBI 517 Expert Systems 28 Inference Process Induction Inductive reasoning forms the basis of scientific discovery. If givenp(a) is true p(b) is true …….. Then we conclude  x, p(x) is true If we observe alex over a period of time and note that whenever he is wet, it turns out that he has gone snorkeling. We might induce that  x, wet(x)  snorkeling(x) Like abduction, induction is also an unsound inference method.

29. UBI 517 Expert Systems 29 Inference Process Monotonic vs non-monotonic reasoning Deductive reasoning is a monotonic reasoning that produce as arguments that preserve truth. Axioms are not allowed to change, since once a fact is known to be true, it is always true and can never be modified or retracted. Most real life problems are non-monotonic quarter(fourth) leading(bucks)  Team [leading(Team)  quarter(fourth)]  strategy(Team, conservative) We can deduce strategy(bucks, conservative) What if the state changes to leading(dolphins)

30. UBI 517 Expert Systems 30 Inference Process RESOLUTION attempts to show that the negation of the statement produces a contradiction with the known statements. winter V summer ~winter V cold  deduce summer V cold In the above example if it is winter the first statement is true if not the second statement is true. From these two we can deduce the third statement to be true

31. UBI 517 Expert Systems 31 Inference Process Conjuctive normal form(Davis, 1960) The steps to convert to conjuctive normal form: 1. Eliminate  by using the fact that a  b is equivalent to ~a V b 2. Reduce the scope of ~ ~(A V B) = ~A  B~(A  B) = ~A V  B DeMorgan's law ~  x P(x) =  x ~P(x) ~  x P(x) =  x ~P(x)

32. UBI 517 Expert Systems 32 Inference Process 3. Standardize variables so that each quantifier binds a unique variable.  x P(x) V  x Q(x) would be converted to  x P(x) V  y Q(y) 4. Move all quantifiers to the left of the formula without changing their relative order.  x  y P(x) V Q(y)

33. UBI 517 Expert Systems 33 Inference Process 5. Eliminate existential quantifiers. A formula that contains an existentially quantified variable asserts that there is a value that can be substituted for the variable that makes the formula true.  y President(y)can be transformed into President(S1)

34. UBI 517 Expert Systems 34 Inference Process 5. If existential quantifiers occur within the scope of a universal quantifier then the value that satisfies the predicate may depend on the values of the universally quantified variables.  x  y fatherof(y, x)can be transformed into  x fatherof(S2(x), x) These generated functions are called Skolem functions.

35. UBI 517 Expert Systems 35 Inference Process 6. Drop the prefix. 7. Convert the matrix into conjunction of disjuncts. 8. Call each conjunct a separate clause.

36. UBI 517 Expert Systems 36 Inference Process The Unification Algorithm: The object of the unification procedure is to discover at least one substitution that causes two literals to match.

37. UBI 517 Expert Systems 37 Inference Process Example: 1. Marcus was a man. man(marcus) 2. Marcus was a Pompeian. pompeian(marcus) 3. All Pompeians were Romans.  x pompeian(X)  roman(X) 4. Caesar is a ruler. ruler(caesar)

38. UBI 517 Expert Systems 38 Inference Process 5. All Romans were either loyal to Caesar or hated him.  x roman(X)  loyalto(X, casear) v hate(X, casear) 6. Everyone is loyal to someone.  X  Y loyalto(X, Y) 7. People only try to assassinate rulers they are not loyal to.  X  Y person(X)  ruler(Y)  tryassassinate(X,Y)  ~loyalto(X, Y) 8. Marcus tried to assassinate Caesar. tryassassinate(marcus, caesar)

39. UBI 517 Expert Systems 39 Inference Process Proof by resolution: Given the axioms in clause form: 1. man(Marcus) 2. Pompeian(Marcus) 3. ~Pompeian(x1) V Roman(x1) 4. ruler(Caesar) 5. ~Roman(x2) V loyalto(x2, Caesar) V hate(x2, Caesar) 6. loyalto(x3, f1(x3)) 7. ~person(x4) V ~ruler(y1) V ~tryassassinate(x4, y1) V ~loyalto(x4, y1) 8. tryassassinate(Marcus, Caesar)

40. UBI 517 Expert Systems 40 Inference Process Prove: hate(Marcus, Caesar)

41. UBI 517 Expert Systems 41 Inference Process Prove: loyalto(Marcus, Caesar)

42. UBI 517 Expert Systems 42 Inference Process Prove: loyalto(Marcus, Caesar) - Continue Suppose our knowledge base contained the two additional statements 9. persecute(x, y)  hate(y, x) ~persecute(x5, y2) V hate(y2, x5) 10. hate(x, y)  persecute(y, x) ~hate(x6, y3) V persecute(y3, x6)

43. UBI 517 Expert Systems 43 Prolog Prolog is the implementation language for predicate logic. Prolog is declarative Three main features are: Facts Rules Backtracking If something is not explicitly stated, Prolog assumes that it is false. Prolog is non-monotonic where it deviates from pure predicate logic.

44. UBI 517 Expert Systems 44 Example child_of(alex, nicole). child_of(alina, nicole). child_of(nicholas, leah). child_of(philip, leah). child_of(melanie, cathy). male(alex). male(philip). male(alex). male(nicholas). female(alina). female(leah). female(nicole). female(angela). sisters(nicole, leah). sisters(X, Z):- child_of(X, Y), child_of(Z, Y), female(X), female(Z)

45. UBI 517 Expert Systems 45 Disadvantages of Predicate Logic as a Basis for Knowledge-Based System Managing uncertainty availability of only two levels of truth – true or false. certainty factors have been implemented in Prolog to mitigate this. Monotonic versus non-monotonic reasoning.

46. UBI 517 Expert Systems 46 Exercises: Consider the following sentences: John likes all kinds of food. Apples are food. Chicken is food. Anything anyone eats and isn't killed by is food. Bill eats peanuts and is still alive. Sue eats everything Bill eats. a. Translate these sentences into formulas in predicate logic. b. Convert the formulas of part a into clause form. c. Prove that John likes peanuts. d. Use resolution to answer the question, "What food does Sue eat?"

47. UBI 517 Expert Systems 47 Exercises: Consider the following logic statements:  x [panther(X)  feline(X)]  x [house_cat(X)  feline(X)]  x [house_cat(X)  docile(X)]  x [feline(X)  carnivore(X)]  x [carnivore(X)  food(X, meat)]  x [horse(X)  herbivore(X)]  x [herbivore(X)  food(X, plants)]

48. UBI 517 Expert Systems 48 Exercises: a)Using modus ponens derive all possible relations from the following facts. panther(sam) house_cat(rubble) lion(leo) b)Using abduction and the following statement what could kitty be? food(kitty, meat) c)Convert all of the logic statements into clauses d) Attempt to prove the following statements using resolution: house_cat(rebel)  food(rebel, meat) horse(wilber)  food(wilber, meat)