Understanding Logic Resolution and Propositional Theorems in Homework Programs

1. For Friday • No new reading • Logic and Resolution Homework

2. Program 2 • Any questions?

3. Resolution • Propositional version. {a Ú b, ¬b Ú c} |- a Ú c OR {¬aÞ b, b Þ c} |- ¬a Þ c Reasoning by cases OR transitivity of implication • First­order form • For two literals pj and qk in two clauses • p1Ú ... pj ... Ú pm • q1Ú ... qk ... Ú qn such that q=UNIFY(pj , ¬qk), derive SUBST(q, p1Ú...pj­1Úpj+1...ÚpmÚq1Ú...qk­1 qk+1...Úqn)

4. Implication form • Can also be viewed in implicational form where all negated literals are in a conjunctive antecedent and all positive literals in a disjunctive conclusion. ¬p1Ú...Ú¬pmÚq1Ú...ÚqnÛ p1Ù... Ù pmÞ q1Ú ...Ú qn

5. Conjunctive Normal Form (CNF) • For resolution to apply, all sentences must be in conjunctive normal form, a conjunction of disjunctions of literals (a1Ú ...Ú am) Ù (b1Ú ... Ú bn) Ù ..... Ù (x1Ú ... Ú xv) • Representable by a set of clauses (disjunctions of literals) • Also representable as a set of implications (INF).

6. Example Initial CNF INF P(x) Þ Q(x) ¬P(x) Ú Q(x) P(x) Þ Q(x) ¬P(x) Þ R(x) P(x) Ú R(x) True Þ P(x) Ú R(x) Q(x) Þ S(x) ¬Q(x) Ú S(x) Q(x) Þ S(x) R(x) Þ S(x) ¬R(x) Ú S(x) R(x) Þ S(x)

7. Resolution Proofs • INF (CNF) is more expressive than Horn clauses. • Resolution is simply a generalization of modus ponens. • As with modus ponens, chains of resolution steps can be used to construct proofs. • Factoring removes redundant literals from clauses • S(A) Ú S(A) -> S(A)

8. Sample Proof P(w)  Q(w) Q(y)  S(y) {y/w} P(w)  S(w) True  P(x)  R(x) {w/x} True  S(x)  R(x) R(z)  S(z) {x/A, z/A} True  S(A)

9. Refutation Proofs • Unfortunately, resolution proofs in this form are still incomplete. • For example, it cannot prove any tautology (e.g. PÚ¬P) from the empty KB since there are no clauses to resolve. • Therefore, use proof by contradiction (refutation, reductio ad absurdum). Assume the negation of the theorem P and try to derive a contradiction (False, the empty clause). • (KB Ù ¬P Þ False) Û KB Þ P

10. Sample Proof P(w)  Q(w) Q(y)  S(y) {y/w} P(w)  S(w) True  P(x)  R(x) {w/x} True  S(x)  R(x) R(z)  S(z) {z/x} S(A)  False True  S(x) {x/A} False

11. Resolution Theorem Proving • Convert sentences in the KB to CNF (clausal form) • Take the negation of the proposed theorem (query), convert it to CNF, and add it to the KB. • Repeatedly apply the resolution rule to derive new clauses. • If the empty clause (False) is eventually derived, stop and conclude that the proposed theorem is true.

12. Conversion to Clausal Form • Eliminate implications and biconditionals by rewriting them. p Þ q -> ¬p Ú q p Û q ­> (¬p Ú q) Ù (p Ú ¬q) • Move ¬ inward to only be a part of literals by using deMorgan's laws and quantifier rules. ¬(p Ú q) -> ¬p Ù ¬q ¬(p Ù q) -> ¬p Ú¬q ¬"x p -> $x ¬p ¬$x p -> "x ¬p ¬¬p -> p

13. Conversion continued • Standardize variables to avoid use of the same variable name by two different quantifiers. "x P(x) Ú$x P(x) -> "x1 P(x1) Ú $x2 P(x2) • Move quantifiers left while maintaining order. Renaming above guarantees this is a truth­preserving transformation. "x1 P(x1) Ú $x2 P(x2) -> "x1$x2 (P(x1) Ú P(x2))

14. Conversion continued • Skolemize: Remove existential quantifiers by replacing each existentially quantified variable with a Skolem constant or Skolem function as appropriate. • If an existential variable is not within the scope of any universally quantified variable, then replace every instance of the variable with the same unique constant that does not appear anywhere else. $x (P(x) Ù Q(x)) -> P(C1) Ù Q(C1) • If it is within the scope of n universally quantified variables, then replace it with a unique n­ary function over these universally quantified variables. "x1$x2(P(x1) Ú P(x2)) -> "x1 (P(x1) Ú P(f1(x1))) "x(Person(x) Þ$y(Heart(y) Ù Has(x,y))) -> "x(Person(x) Þ Heart(HeartOf(x)) Ù Has(x,HeartOf(x))) • Afterwards, all variables can be assumed to be universally quantified, so remove all quantifiers.

15. Conversion continued • Distribute Ù over Ú to convert to conjunctions of clauses (aÙb) Ú c -> (aÚc) Ù (bÚc) (aÙb) Ú (cÙd) -> (aÚc) Ù (bÚc) Ù (aÚd) Ù (bÚd) • Can exponentially expand size of sentence. • Flatten nested conjunctions and disjunctions to get final CNF (a Ú b) Ú c -> (a Ú b Ú c) (a Ù b) Ù c -> (a Ù b Ù c) • Convert clauses to implications if desired for readability (¬a Ú ¬b Ú c Ú d) -> a Ù b Þ c Ú d

16. Sample Clause Conversion "x((Prof(x) Ú Student(x)) Þ($y(Class(y) Ù Has(x,y)) Ù$y(Book(y) Ù Has(x,y)))) "x(¬(Prof(x) Ú Student(x)) Ú($y(Class(y) Ù Has(x,y)) Ù$y(Book(y) Ù Has(x,y)))) "x((¬Prof(x) Ù ¬Student(x)) Ú ($y(Class(y) Ù Has(x,y)) Ù$y(Book(y) Ù Has(x,y)))) "x((¬Prof(x) Ù ¬Student(x)) Ú ($y(Class(y) Ù Has(x,y)) Ù$z(Book(z) Ù Has(x,z)))) "x$y$z((¬Prof(x)Ù¬Student(x))Ú ((Class(y) Ù Has(x,y)) Ù (Book(z) Ù Has(x,z)))) (¬Prof(x)Ù¬Student(x))Ú (Class(f(x)) Ù Has(x,f(x)) Ù Book(g(x)) Ù Has(x,g(x))))

17. Clause Conversion (¬Prof(x)Ù¬Student(x))Ú (Class(f(x)) Ù Has(x,f(x)) Ù Book(g(x)) Ù Has(x,g(x)))) (¬Prof(x) Ú Class(f(x))) Ù (¬Prof(x) Ú Has(x,f(x))) Ù (¬Prof(x) Ú Book(g(x))) Ù (¬Prof(x) Ú Has(x,g(x))) Ù (¬Student(x) Ú Class(f(x))) Ù (¬Student(x) Ú Has(x,f(x))) Ù (¬Student(x) Ú Book(g(x))) Ù (¬Student(x) Ú Has(x,g(x))))

18. Sample Resolution Problem • Jack owns a dog. • Every dog owner is an animal lover. • No animal lover kills an animal. • Either Jack or Curiosity killed Tuna the cat. • Did Curiosity kill the cat?

19. In Logic Form A) $x Dog(x) Ù Owns(Jack,x) B) "x ($y Dog(y) Ù Owns(x,y)) Þ AnimalLover(x)) C) "x AnimalLover(x) Þ ("y Animal(y) Þ ¬Kills(x,y)) D) Kills(Jack,Tuna) Ú Kills(Cursiosity,Tuna) E) Cat(Tuna) F) "x(Cat(x) Þ Animal(x)) Query: Kills(Curiosity,Tuna)

20. In Normal Form A1) Dog(D) A2) Owns(Jack,D) B) Dog(y) Ù Owns(x,y) Þ AnimalLover(x) C) AnimalLover(x) Ù Animal(y) Ù Kills(x,y) Þ False D) Kills(Jack,Tuna) Ú Kills(Curiosity,Tuna) E) Cat(Tuna) F) Cat(x) Þ Animal(x) Query: Kills(Curiosity,Tuna) Þ False

21. Resolution Proof

22. Search • What are characteristics of good problems for search? • What does the search know about the goal state? • Consider the package problem on the exam: • How well would search REALLY work on that problem?

23. Search vs. Planning • Planning systems: • Open up action and goal representation to allow selection • Divide and conquer by subgoaling • Relax the requirement for sequential construction of solutions

24. Planning in Situation Calculus PlanResult(p,s) is the situation resulting from executing p in s PlanResult([],s) = s PlanResult([a|p],s) = PlanResult(p,Result(a,s)) Initial stateAt(Home,S_0) Have(Milk,S_0)  … Actions as Successor State axioms Have(Milk,Result(a,s)) [(a=Buy(Milk) At(Supermarket,s))  Have(Milk,s) a  ...)] Querys=PlanResult(p,S_0)At(Home,s)Have(Milk,s) … Solutionp = Go(Supermarket),Buy(Milk),Buy(Bananas),Go(HWS),…] • Principal difficulty: unconstrained branching, hard to apply heuristics

25. The Blocks World • We have three blocks A, B, and C • We can know things like whether a block is clear (nothing on top of it) and whether one block is on another (or on the table) • Initial State: • Goal State: A B C A B C

26. Situation Calculus in Prolog holds(on(A,B),result(puton(A,B),S)) :­ holds(clear(A),S), holds(clear(B),S), neq(A,B). holds(clear(C),result(puton(A,B),S)) :­ holds(clear(A),S), holds(clear(B),S), holds(on(A,C),S), neq(A,B). holds(on(X,Y),result(puton(A,B),S)) :­ holds(on(X,Y),S), neq(X,A), neq(Y,A), neq(A,B). holds(clear(X),result(puton(A,B),S)) :­ holds(clear(X),S), neq(X,B). holds(clear(table),S).

27. neq(a,table). neq(table,a). neq(b,table). neq(table,b). neq(c,table). neq(table,c). neq(a,b). neq(b,a). neq(a,c). neq(c,a). neq(b,c). neq(c,b).

28. Situation Calculus Planner plan([],_,_). plan([G1|Gs], S0, S) :­ holds(G1,S), plan(Gs, S0, S), reachable(S,S0). reachable(S,S). reachable(result(_,S1),S) :­ reachable(S1,S). • However, what will happen if we try to make plans using normal Prolog depth­first search?

29. Stack of 3 Blocks holds(on(a,b), s0). holds(on(b,table), s0). holds(on(c,table),s0). holds(clear(a), s0). holds(clear(c), s0). | ?­ cpu_time(db_prove(6,plan([on(a,b),on(b,c)],s0,S)), T). S = result(puton(a,b),result(puton(b,c),result(puton(a,table),s0))) T = 1.3433E+01

30. Invert stack holds(on(a,table), s0). holds(on(b,a), s0). holds(on(c,b),s0). holds(clear(c), s0). ?­ cpu_time(db_prove(6,plan([on(b,c),on(a,b)],s0,S)),T). S = result(puton(a,b),result(puton(b,c),result(puton(c,table),s0))), T = 7.034E+00

31. Simple Four Block Stack holds(on(a,table), s0). holds(on(b,table), s0). holds(on(c,table),s0). holds(on(d,table),s0). holds(clear(c), s0). holds(clear(b), s0). holds(clear(a), s0). holds(clear(d), s0). | ?­ cpu_time(db_prove(7,plan([on(b,c),on(a,b),on(c,d)],s0,S)),T). S = result(puton(a,b),result(puton(b,c),result(puton(c,d),s0))), T = 2.765935E+04 7.5 hours!

32. STRIPS • Developed at SRI (formerly Stanford Research Institute) in early 1970's. • Just using theorem proving with situation calculus was found to be too inefficient. • Introduced STRIPS action representation. • Combines ideas from problem solving and theorem proving. • Basic backward chaining in state space but solves subgoals independently and then tries to reachieve any clobbered subgoals at the end.

33. STRIPS Representation • Attempt to address the frame problem by defining actions by a precondition, and add list, and a delete list. (Fikes & Nilsson, 1971). • Precondition: logical formula that must be true in order to execute the action. • Add list: List of formulae that become true as a result of the action. • Delete list: List of formulae that become false as result of the action.

34. Sample Action • Puton(x,y) • Precondition: Clear(x) Ù Clear(y) Ù On(x,z) • Add List: {On(x,y), Clear(z)} • Delete List: {Clear(y), On(x,z)}

35. STRIPS Assumption • Every formula that is satisfied before an action is performed and does not belong to the delete list is satisfied in the resulting state. • Although Clear(z) implies that On(x,z) must be false, it must still be listed in the delete list explicitly. • For action Kill(x,y) must put Alive(y), Breathing(y), Heart­Beating(y), etc. must all be included in the delete list although these deletions are implied by the fact of adding Dead(y)

36. Subgoal Independence • If the goal state is a conjunction of subgoals, search is simplified if goals are assumed independent and solved separately (divide and conquer) • Consider a goal of A on B and C on D from 4 blocks all on the table

37. Subgoal Interaction • Achieving different subgoals may interact, the order in which subgoals are solved in this case is important. • Consider 3 blocks on the table, goal of A on B and B on C • If do puton(A,B) first, cannot do puton(B,C) without undoing (clobbering) subgoal: on(A,B)

38. Sussman Anomaly • Goal of A on B and B on C • Starting state of C on A and B on table • Either way of ordering subgoals causes clobbering

39. STRIPS Approach • Use resolution theorem prover to try and prove that goal or subgoal is satisfied in the current state. • If it is not, use the incomplete proof to find a set of differences between the current and goal state (a set of subgoals). • Pick a subgoal to solve and an operator that will achieve that subgoal. • Add the precondition of this operator as a new goal and recursively solve it.

40. STRIPS Algorithm STRIPS(init­state, goals, ops) Let current­state be init­state; For each goal in goals do If goal cannot be proven in current state Pick an operator instance, op, s.t. goal Î adds(op); /* Solve preconditions */ STRIPS(current­state, preconds(op), ops); /* Apply operator */ current­state := current­state + adds(op) ­ dels(ops); /* Patch any clobbered goals */ Let rgoals be any goals which are not provable in current­state; STRIPS(current­state, rgoals, ops).

41. Algorithm Notes • The “pick operator instance” step involves a nondeterministic choice that is backtracked to if a dead­end is ever encountered. • Employs chronological backtracking (depth­first search), when it reaches a dead­end, backtrack to last decision point and pursue the next option.

42. Norvig’s Implementation • Simple propositional (no variables) Lisp implementation of STRIPS. #S(OP ACTION (MOVE C FROM TABLE TO B) PRECONDS ((SPACE ON C) (SPACE ON B) (C ON TABLE)) ADD­LIST ((EXECUTING (MOVE C FROM TABLE TO B)) (C ON B)) DEL­LIST ((C ON TABLE) (SPACE ON B))) • Commits to first sequence of actions that achieves a subgoal (incomplete search). • Prefers actions with the most preconditions satisfied in the current state. • Modified to to try and re-achieve any clobbered subgoals (only once).

43. STRIPS Results ; Invert stack (good goal ordering) > (gps '((a on b)(b on c) (c on table) (space on a) (space on table)) '((b on a) (c on b))) Goal: (B ON A) Consider: (MOVE B FROM C TO A) Goal: (SPACE ON B) Consider: (MOVE A FROM B TO TABLE) Goal: (SPACE ON A) Goal: (SPACE ON TABLE) Goal: (A ON B) Action: (MOVE A FROM B TO TABLE)

44. Goal: (SPACE ON A) Goal: (B ON C) Action: (MOVE B FROM C TO A) Goal: (C ON B) Consider: (MOVE C FROM TABLE TO B) Goal: (SPACE ON C) Goal: (SPACE ON B) Goal: (C ON TABLE) Action: (MOVE C FROM TABLE TO B) ((START) (EXECUTING (MOVE A FROM B TO TABLE)) (EXECUTING (MOVE B FROM C TO A)) (EXECUTING (MOVE C FROM TABLE TO B)))

45. ; Invert stack (bad goal ordering) > (gps '((a on b)(b on c) (c on table) (space on a) (space on table)) '((c on b)(b on a))) Goal: (C ON B) Consider: (MOVE C FROM TABLE TO B) Goal: (SPACE ON C) Consider: (MOVE B FROM C TO TABLE) Goal: (SPACE ON B) Consider: (MOVE A FROM B TO TABLE) Goal: (SPACE ON A) Goal: (SPACE ON TABLE) Goal: (A ON B) Action: (MOVE A FROM B TO TABLE) Goal: (SPACE ON TABLE) Goal: (B ON C) Action: (MOVE B FROM C TO TABLE)

46. Goal: (SPACE ON B) Goal: (C ON TABLE) Action: (MOVE C FROM TABLE TO B) Goal: (B ON A) Consider: (MOVE B FROM TABLE TO A) Goal: (SPACE ON B) Consider: (MOVE C FROM B TO TABLE) Goal: (SPACE ON C) Goal: (SPACE ON TABLE) Goal: (C ON B) Action: (MOVE C FROM B TO TABLE) Goal: (SPACE ON A) Goal: (B ON TABLE) Action: (MOVE B FROM TABLE TO A)

47. Must reachieve clobbered goals: ((C ON B)) Goal: (C ON B) Consider: (MOVE C FROM TABLE TO B) Goal: (SPACE ON C) Goal: (SPACE ON B) Goal: (C ON TABLE) Action: (MOVE C FROM TABLE TO B) ((START) (EXECUTING (MOVE A FROM B TO TABLE)) (EXECUTING (MOVE B FROM C TO TABLE)) (EXECUTING (MOVE C FROM TABLE TO B)) (EXECUTING (MOVE C FROM B TO TABLE)) (EXECUTING (MOVE B FROM TABLE TO A)) (EXECUTING (MOVE C FROM TABLE TO B)))

48. STRIPS on Sussman Anomaly > (gps '((c on a)(a on table)( b on table) (space on c) (space on b) (space on table)) '((a on b)(b on c))) Goal: (A ON B) Consider: (MOVE A FROM TABLE TO B) Goal: (SPACE ON A) Consider: (MOVE C FROM A TO TABLE) Goal: (SPACE ON C) Goal: (SPACE ON TABLE) Goal: (C ON A) Action: (MOVE C FROM A TO TABLE) Goal: (SPACE ON B) Goal: (A ON TABLE) Action: (MOVE A FROM TABLE TO B) Goal: (B ON C)

49. Consider: (MOVE B FROM TABLE TO C) Goal: (SPACE ON B) Consider: (MOVE A FROM B TO TABLE) Goal: (SPACE ON A) Goal: (SPACE ON TABLE) Goal: (A ON B) Action: (MOVE A FROM B TO TABLE) Goal: (SPACE ON C) Goal: (B ON TABLE) Action: (MOVE B FROM TABLE TO C) Must reachieve clobbered goals: ((A ON B)) Goal: (A ON B) Consider: (MOVE A FROM TABLE TO B)

50. Goal: (SPACE ON A) Goal: (SPACE ON B) Goal: (A ON TABLE) Action: (MOVE A FROM TABLE TO B) ((START) (EXECUTING (MOVE C FROM A TO TABLE)) (EXECUTING (MOVE A FROM TABLE TO B)) (EXECUTING (MOVE A FROM B TO TABLE)) (EXECUTING (MOVE B FROM TABLE TO C)) (EXECUTING (MOVE A FROM TABLE TO B)))