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Artificial Intelligence 9. Resolution Theorem Proving Course V231 Department of Computing Imperial College Jeremy Gow.

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1 Artificial Intelligence 9. Resolution Theorem Proving Course V231 Department of Computing Imperial College Jeremy Gow

2 The Full Resolution Rule If Unify(P j, ¬Q k ) = (¬ makes them unifiable) P 1 … P m, Q 1 … Q n Subst(, P 1 … (no P j ) … P m Q 1 … (no Q k )... Q n ) P j and Q k are resolved Arbitrary number of disjuncts Relies on preprocessing into CNF

3 A More Concise Version E.g. for A = {1, 2, 7} first clause is L 1 L 2 L 7

4 Resolution Proving Knowledge base of clauses – Start with the axioms and negation of theorem in CNF Resolve pairs of clauses – Using single rule of inference (full resolution) – Resolved sentence contains fewer literals Proof ends with the empty clause – Signifies a contradiction – Must mean the negated theorem is false (Because the axioms are consistent) – Therefore the original theorem was true

5 Empty Clause means False Resolution theorem proving ends – When the resolved clause has no literals (empty) This can only be because: – Two unit clauses were resolved One was the negation of the other (after substitution) – Example: q(X) and ¬q(X) or: p(X) and ¬p(bob) Hence if we see the empty clause – This was because there was an inconsistency – Hence the proof by refutation

6 Resolution as Search Initial State: Knowledge base (KB) of axioms and negated theorem in CNF Operators: Resolution rule picks 2 clauses and adds new clause Goal Test: Does KB contain the empty clause? Search space of KB states We want proof (path) or just checking (artefact)

7 Aristotles Example (Again) Socrates is a man and all men are mortal Therefore Socrates is mortal Initial state 1) is_man(socrates) 2) is_man(X) is_mortal(X) 3) ¬is_mortal(socrates) (negation of theorem) Resolving (1) & (2) gives new state (1)-(3) & 4) is_mortal(socrates)

8 Aristotles Example: Search Space 1) is_man(socrates) 2) is_man(X) is_mortal(X) 3) ¬is_mortal(socrates) 4) is_mortal(socrates) 1) is_man(socrates) 2) ¬is_man(X) is_mortal(X) 3) ¬is_mortal(socrates) 4) ¬is_man(socrates) 1) is_man(socrates) 2) is_man(X) is_mortal(X) 3) ¬is_mortal(socrates) 1) is_man(socrates) 2) is_man(X) is_mortal(X) 3) ¬is_mortal(socrates) 4) is_mortal(socrates) 5) False 1) is_man(socrates) 2) is_man(X) is_mortal(X) 3) ¬is_mortal(socrates) 4) ¬is_man(socrates) 5) False

9 Resolution Proof Tree (Proof 1)

10 Resolution Proof Tree (Proof 2)

11 Reading Proof Tree 2 You said that all men were mortal. That means that for all things X, either X is not a man, or X is mortal [CNF step]. If we assume that Socrates is not mortal, then, given your previous statement, this means Socrates is not a man [first resolution step]. But you said that Socrates is a man, which means that our assumption was false [second resolution step], so Socrates must be mortal.

12 Russell & Norvig Example

13 Reminder: Kowalski NF Can reintroduce to CNF, e.g. ¬A ¬C B becomes (A C) B Kowalski normal form (A 1 … A n ) (B 1 … B n ) Resolve in KNF using KNF style rules – e.g. Binary resolution… A B, B C A C

14 R&N Example: Kowalski NF

15 R&N Example: Proof Tree

16 R&N Example: Prover9 Input

17 R&N Example: Prover9 Proof

18 Equality Axioms is_pres(obama) and is_pres(b_obama) – will not unify (syntactically different) unification algorithm does not allow this – Even if we add to the knowledge base: obama = b_obama Solution: add equality axioms to KB – X=X, X=Y Y=X, etc. – Special axiom for every predicate/function: X = Y P(X) = P(Y)

19 Equality & Demodulation Alternative solution: rewrite with equalities Demodulation inference rule X=Y, A[S] Subst(, A[Y]) – Two input clauses (one an equality X=Y) – Unify X with a subterm S of other – Apply unifier to clause with subterm Y (not S) – Also works unifying with Y and putting in X Unify(X, S) =

20 Heuristic Strategies Pure resolution search tends to be slow For interesting problems – Many clauses in the initial knowledge base – Each step adds a new clause (which can be used) – Num. of possible resolution combinations explodes Selection Heuristics – Intelligently choose which pair to resolve Pruning Heuristics – Forbid certain pairs

21 Unit Preference Strategy Prefer to resolve unit clauses – Contain only a single literal – Selection heuristic Searching for smallest (empty) clause – Resolving with the unit clauses keeps small Very effective early on for simple problems – Doesnt reduce branching rate for medium problems

22 Set of Support Strategy Distinguished subset of KB clauses – Set of support (SOS) clauses – Every step must involve SOS (pruning heuristic) Must be careful not to lose completeness Example SOS strategy: – Initial SOS is negated theorem – Add new clauses to SOS – Hence False will be deduced (strategy is complete) Many provers use SOS, e.g. Prover9

23 Input Resolution Strategy Special case of SOS strategy – SOS = clauses in the initial knowledge base Clearly reduces search space – Every resolution must involve an original clause – So number of possible resolutions grows slowly Not complete for first order logic But complete for Horn-clauses, e.g. Prolog

24 Subsumption Clause C subsumes clause D – if C is more general (D is more specific) Naive check for subsumption – Select C2, a subset of literals of C – Find Unify(C2, D) = – does not add anything to D (only renames vars) Example: – p(george) q(X) subsumed by p(A) q(B) r(C) – Substitution: {A/george, X/B} – Second clause is more general

25 Subsumption Strategy Check each new clause is not subsumed by KB Complete strategy – Specific clauses can be inferred from general ones – So we can throw specific clauses away – Reduced search space still contains False Can be inefficient – expense must be outweighed by the reduction in the search space

26 Applications: Axioms for Algebras Bill McCune and Larry Wos – Argonne National Laboratories – FO resolution provers: EQP, Otter, Prover9 Robbins Problem (axioms of Boolean algebras) – Stated 60+ years ago, mathematicians failed – 1996: EQP solved in 8 days in 1996 (+human work) General application to algebraic axiomatisations – Generate possible axioms for algebras – Prove new axioms equivalent to old

27 Applications: Theory Formation Simons HR system: Automated Theory Formation – Used in mathematical (and bioinformatics) domains Theories = concepts, examples, conjectures, proofs HR uses Otter to prove conjectures it makes Effective in algebraic domains – See notes for anti-associative algebra results Otter not so effective in number theory – Used as a triviality filter (discard theorems it can prove) – Example conjectures made by HR (and proved by Simon): Sum of divisors is prime number of divisors is prime Sum of divisors of a square is an odd number Perfect numbers are pernicious [and many more…..]

28 Inductive Theorem Proving Deduction by mathematical induction Induction over many different structures Allows reasoning about recursion/iteration – Useful for hardware/software verification Dont confuse inductive learning (next lecture)

29 Interactive Theorem Proving Necessary to interact with humans in order to prove theorems of any difficulty Mathematicians assistant – Let a theorem prover do simple tasks while you develop a theory (e.g., Buchbergers Theorema) Guided theorem prover – User follows and guides computer proof attempt – Needs visualisation tools for proof trees

30 Higher Order Theorem Proving Deduction in higher order logics – See lecture 4 – Allows more natural and succinct statements – Logics much less well-behaved HOL theorem prover – Larry Paulsons group in Cambridge – Has been used for verification tasks E.g. verification of crytographic protocols – Uses induction and interactive control

31 Proof Planning Initially Alan Bundys group in Edinburgh Human proofs often follow a similar structure – Express this as a outline plan – Methods represent a patterns of deduction Outline plan guides proof search – Results in specific plan for theorem – Critics deal with common problems Particularly useful for inductive theorems – Proof of base case and step case follow pattern

32 Databases & Competitions TPTP library (Sutcliffe & Suttner) – Thousands of Problems for Theorem Provers – Benchmarks for first order provers – HR is only non-human to add to this library Annual CASC competition (Sutcliffe et al.) – Which is fastest/most accurate FO prover on planet? – Uses blind selection from the TPTP library – champion: Vampire (Voronkov & Riazonov)

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