Inf2D 12: Resolution-Based Inference
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1 School of Informatics, University of Edinburgh 09/02/18 Slide Credits: Jacques Fleuriot, Michael Rovatsos, Michael Herrmann
2 Last time Unification: Given α and β, find θ such that αθ = βθ Most general unifier (MGU) Generalised Modus Ponens p 1, p 2,..., p n (p 1 p 2 p n q) qθ when p i θ p iθ i
3 Outline Forward chaining Backward chaining Resolution
4 Forward chaining algorithm
5 West Knowledge Base American(x) Weapon(y) Sells(x, y, z) Hostile(z) Criminal(x) Owns(Nono, M 1 ) and Missile(M 1 ) Missile(x) Owns(Nono, x) Sells(West, x, Nono) Missile(x) Weapon(x) Enemy(x, America) Hostile(x) American(West) and Enemy(Nono, America)
6 Forward chaining proof American(x) Weapon(y) Sells(x, y, z) Hostile(z) Criminal(x) Owns(Nono, M 1 ) and Missile(M 1 ) Missile(x) Owns(Nono, x) Sells(West, x, Nono) Missile(x) Weapon(x) Enemy(x, America) Hostile(x) American(West) and Enemy(Nono, America)
7 Forward chaining proof American(x) Weapon(y) Sells(x, y, z) Hostile(z) Criminal(x) Owns(Nono, M 1 ) and Missile(M 1 ) Missile(x) Owns(Nono, x) Sells(West, x, Nono) Missile(x) Weapon(x) Enemy(x, America) Hostile(x) American(West) and Enemy(Nono, America)
8 Forward chaining proof American(x) Weapon(y) Sells(x, y, z) Hostile(z) Criminal(x) Owns(Nono, M 1 ) and Missile(M 1 ) Missile(x) Owns(Nono, x) Sells(West, x, Nono) Missile(x) Weapon(x) Enemy(x, America) Hostile(x) American(West) and Enemy(Nono, America)
9 Properties of forward chaining II Sound and complete for first-order definite clauses Definite clause = exactly one positive literal. Datalog = first-order definite clauses + no functions FC terminates for Datalog in finite number of iterations May not terminate in general if α is not entailed This is unavoidable: entailment with definite clauses is semi-decidable
10 Efficiency of forward chaining I Incremental forward chaining: no need to match a rule on iteration k if a premise wasn t added on iteration k 1 match each rule whose premise contains a newly added positive literal Matching itself can be expensive: Database indexing allows O(1) retrieval of known facts e.g., query Missile(x) retrieves Missile(M1) Forward chaining is widely used in deductive databases
11 Efficiency of forward chaining II Pattern Matching For each θ such that SUBST (θ, p 1 p n ) = SUBST (θ, p 1 p n) for some p 1,..., p n in KB Finding all possible unifiers can be very expensive Example Missile(x) Owns(Nono, x) Sells(West, x, Nono) Can find each object owned by Nono in constant time and then check if it is admissible But what if Nono owns many objects but very few missiles? Conjunct Ordering: Better (cost-wise) to find all missiles first and then check whether they are owned by Nono Optimal ordering is NP-hard. Heuristics available: e.g. MRV from CSP if each conjunct is viewed as a constraint on its vars
12 Hard matching example Diff (wa, nt) Diff (wa, sa) Diff (nt, q) Diff (nt, sa) Diff (q, nsw) Diff (q, sa) Diff (nsw, v) Diff (nsw, sa) Diff (v, sa) Colourable Diff (Red, Blue) Diff (Red, Green) Diff (Green, Red) Diff (Green, Blue) Diff (Blue, Red) Diff (Blue, Green) Every finite domain CSP can be expressed as a single definite clause + ground facts Colourable is inferred iff the CSP has a solution CSPs include 3SAT as a special case, hence matching is NP-hard
13 Backward chaining algorithm SUBST (COMPOSE(θ 1, θ 2 ), p) = SUBST (θ 2, SUBST (θ 1, p))
14 Backward chaining example American(x) Weapon(y) Sells(x, y, z) Hostile(z) Criminal(x) Owns(Nono, M 1 ) and Missile(M 1 ) Missile(x) Owns(Nono, x) Sells(West, x, Nono) Missile(x) Weapon(x) Enemy(x, America) Hostile(x) American(West) and Enemy(Nono, America)
15 Backward chaining example American(x) Weapon(y) Sells(x, y, z) Hostile(z) Criminal(x) Owns(Nono, M 1 ) and Missile(M 1 ) Missile(x) Owns(Nono, x) Sells(West, x, Nono) Missile(x) Weapon(x) Enemy(x, America) Hostile(x) American(West) and Enemy(Nono, America)
16 Backward chaining example American(x) Weapon(y) Sells(x, y, z) Hostile(z) Criminal(x) Owns(Nono, M 1 ) and Missile(M 1 ) Missile(x) Owns(Nono, x) Sells(West, x, Nono) Missile(x) Weapon(x) Enemy(x, America) Hostile(x) American(West) and Enemy(Nono, America)
17 Backward chaining example American(x) Weapon(y) Sells(x, y, z) Hostile(z) Criminal(x) Owns(Nono, M 1 ) and Missile(M 1 ) Missile(x) Owns(Nono, x) Sells(West, x, Nono) Missile(x) Weapon(x) Enemy(x, America) Hostile(x) American(West) and Enemy(Nono, America)
18 Backward chaining example American(x) Weapon(y) Sells(x, y, z) Hostile(z) Criminal(x) Owns(Nono, M 1 ) and Missile(M 1 ) Missile(x) Owns(Nono, x) Sells(West, x, Nono) Missile(x) Weapon(x) Enemy(x, America) Hostile(x) American(West) and Enemy(Nono, America)
19 Backward chaining example American(x) Weapon(y) Sells(x, y, z) Hostile(z) Criminal(x) Owns(Nono, M 1 ) and Missile(M 1 ) Missile(x) Owns(Nono, x) Sells(West, x, Nono) Missile(x) Weapon(x) Enemy(x, America) Hostile(x) American(West) and Enemy(Nono, America)
20 Backward chaining example American(x) Weapon(y) Sells(x, y, z) Hostile(z) Criminal(x) Owns(Nono, M 1 ) and Missile(M 1 ) Missile(x) Owns(Nono, x) Sells(West, x, Nono) Missile(x) Weapon(x) Enemy(x, America) Hostile(x) American(West) and Enemy(Nono, America)
21 Properties of backward chaining Depth-first recursive proof search: space is linear in size of proof Incomplete due to infinite loops partial fix by checking current goal against every goal on stack Inefficient due to repeated subgoals (both success and failure) fix using caching of previous results (extra space) Widely used for logic programming
22 Resolution A method for telling whether a propositional formula is satisfiable and for proving that a first-order formula is unsatisfiable. Yields a complete inference algorithm If a set of clauses if unsatisfiable, then the resolution closure of those clauses contains the empty clause (propositional logic)
23 Ground Binary Resolution Soundness: C P iff C P D P iff P D Therefore, C D, C P D P C D which is equivalent to C D Note: if both C and D are empty then resolution deduces the empty clause, i.e. false.
24 Non-Ground Binary Resolution C P D P (C D) θ where θ is the MGU of P and P The two clauses are assumed to be standardized apart so that they share no variables. Soundness: apply θ to premises then appeal to ground binary resolution. Cθ Pθ Dθ Pθ Cθ Dθ
25 Example with θ = {x/ken} Rich (x) Unhappy (x) Unhappy(Ken) Rich (Ken)
26 Factoring More generally A B A B A A C P 1... P m (C P 1 ) θ where θ is the MGU of the P i Soundness: by universal instantiation and deletion of duplicates.
27 Full Resolution C P 1... P m D P 1... P n (C D) θ where θ is MGU of all P i and P j Soundness: by combination of factoring and binary resolution. To prove α: apply resolution steps to CNF (KB α) complete for FOL, if full resolution or binary resolution + factoring is used
28 Conversion to CNF Example: Everyone who loves all animals is loved by someone: x.[ y.animal(y) Loves(x, y)] [ y.loves(y, x)] 1 Eliminate all biconditionals and implications x. [ y. Animal(y) Loves(x, y)] [ y.loves(y, x)] 2 Move inwards, use: x.p x. p, x.p x. p, etc. x.[ y. ( Animal(y) Loves(x, y))] [ y.loves(y, x)] x.[ y. Animal(y) Loves(x, y)] [ y.loves(y, x)] x.[ y.animal(y) Loves(x, y)] [ y.loves(y, x)]
29 Conversion to CNF contd. 1 Standardize variables apart: each quantifier should use a different one x.[ y.animal(y) Loves(x, y)] [ z.loves(z, x)] 2 Skolemize: a more general form of existential instantiation Each existential variable is replaced by a Skolem function of the enclosing universally quantified variables 1) : x.[animal(f (x)) Loves(x, F (x))] Loves(G(x), x) 3 Drop universal quantifiers: [Animal(F (x)) Loves(x, F (x))] Loves(G(x), x) 4 Distribute over : [Animal(F (x)) Loves(G(x), x)] [ Loves(x, F (x)) Loves(G(x), x)] 1) No enclosing universal quantifier? Just replace with Skolem constant, i.e. a function with no argument.
30 West Clauses American(x) Weapon(y) Sells(x, y, z) Hostile(z) Criminal(x) Owns(Nono, M 1 ) and Missile(M 1 ) Missile(x) Owns(Nono, x) Sells(West, x, Nono) Missile(x) Weapon(x) Enemy(x, America) Hostile(x) American(West) and Enemy(Nono, America)
31 Resolution proof: definite clauses
32 Summary Forward chaining Backward chaining Resolution
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