Performance of OSHL on Problems Requiring Definition Expansion

1. Performance of OSHL on Problems Requiring Definition Expansion Swaha Miller David A. Plaisted UNC Chapel Hill

2. How do humans prove theorems? • Semantics • Case analysis • Sequential search through space of possible structures • Focus on the theorem

3. “Systematic methods can now routinely solve verification problems with thousands or tens of thousands of variables, while local search methods can solve hard random 3SAT problems with millions of variables.” (from a conference announcement)

4. DPLL Example {p,r},{p,q,r},{p,r} p=T p=F {T,r},{T,q,r},{T,r} {F,r},{F,q,r},{F,r} SIMPLIFY SIMPLIFY {q,r} {r},{r} SIMPLIFY {}

5. Hyper Linking

6. Eliminating Duplication with the Hyper-Linking Strategy, Shie-Jue Lee and David A. Plaisted, Journal of Automated Reasoning 9 (1992) 25-42.

7. Later propositional strategies • Billon’s disconnection calculus, derived from hyper-linking • Disconnection calculus theorem prover (DCTP), derived from Billon’s work • FDPLL (Model Evolution Calculus)

8. Performance of DCTP on TPTP, 2003 • DCTP 1.3 first in EPS and EPR (largely propositional) • DCTP 10.2p third in FNE (first-order, no equality) solving same number as best provers • DCTP 10.2p fourth in FOF and FEQ (all first-order formulae, and formulae with equality) • DCTP 1.3 is a single strategy prover.

9. Strategy Selection in E

10. Strategy Selection • Schulz, Stephan, E-A Brainiac Theorem Prover, Journal of AI Communications 15(2/3):111-126, 2002.

11. Strategy Selection • The Vampire kernel provides a fairly large number of features for strategy selection. The most important ones are: • Choice of the main saturation procedure : (i) OTTER loop, with or without the Limited Resource Strategy, (ii) DISCOUNT loop. • A variety of optional simplifications. • Parameterised reduction orderings. • A number of built-in literal selection functions and different modes of comparing literals. • Age-weight ratio that specifies how strongly lighter clauses are preferred for inference selection. • Set-of-support strategy.

12. Strategy Selection • The automatic mode of Vampire 7.0 is derived from extensive experimental data obtained on problems from TPTP v2.6.0. Input problems are classified taking into account simple syntactic properties, such as being Horn or non-Horn, presence of equality, etc. Additionally, we take into account the presence of some important kinds of axioms, such as set theory axioms, associativity and commutativity. Every class of problems is assigned a fixed schedule consisting of a number of kernel strategies called one by one with different time limits.

13. DCTP Strategy Selection • DCTP 1.31 has been implemented as a monolithic system in the Bigloo dialect of the Scheme language. • DCTP 1.31 is a single strategy prover. Individual strategies are started by DCTP 10.21p using the schedule based resource allocation scheme known from the E-SETHEO system. Of course, different schedules have been precomputed for the syntactic problem classes. The problem classes are more or less identical with the sub-classes of the competition organisers. • In CASC-J2 DCTP 10.21p performed substantially better.

14. Goal of OSHL • First-order logic • Clause form • Propositional efficiency • Semantics • Requires ground decidability

15. Structure of OSHL • Goal sensitivity if semantics chosen properly • Choose initial semantics to satisfy axioms • Use of natural semantics • For group theory problems, can specify a group • Sequential search through possible interpretations • Thus similar to Davis and Putnam’s method • Propositional Efficiency • Constructs a semantic tree

16. I0 I1 I2 I3 … D0 D1 D2 T • unsatisfiable Ordered Semantic Hyperlinking (Oshl) • Reduce first-order logic problem to propositional problem • Imports propositional efficiency into first-order logic • The algorithm • Imposes an ordering on clauses • Progresses by generating ground instances Di of input clauses and refining interpretations

17. Semantics • Trivial semantics: • Positive: Choose I0 to falsify all atoms, first D is all positive. Forward chaining. • Negative: Choose I0 to satisfy all atoms, first D is all negative. Backward chaining. • Natural semantics: I0 chosen by user

18. Semantics Ordering • <t a well founded ordering on atoms, extended to literals • Extend <t to interpretations as follows: • I and J agree on L if they interpret L the same • Suppose I0 is given • I <t J if I and J are not identical, A is the minimal atom on which they disagree, and I agrees with I0 on A

19. Rules of OSHL Start with empty sequence (C1,C2, …, Cn), D minimal ground instance of an input clause that contradicts I, I minimal model of sequence (C1,C2, …, Cn,D) (C1,C2, …, Cn, D), Cn “out of order” (C1,C2, …, Cn-1,D) (C1,C2, …, Cn,D), max resolution possible (C1,C2, …, Cn-1,res(Cn,D,L)) Proof if empty clause derived

20. ╨ Propositional Example (p I0 p) () ({-p1, -p2, -p3}) I0[-p3] ({-p1, -p2, -p3}, {-p4, -p5, -p6}) I0[-p3,-p6] ({…}, {…}, {-p7}) I0[-p3,-p6,-p7] ({…}, {…}, {-p7}, {p3, p7}) ({…}, {-p4, -p5, -p6}, {p3}) ({-p1, -p2, -p3},{p3}) ({-p1, -p2 }) I0[-p2]

21. U Rules • Choose clauses instances to match existing literals. Look for a contradiction. • Basic clauses and U clauses • Basic clauses are used in three rules given • Sequence can also have U clauses on the end • U clauses have a selected literal • In basic clauses the max. lit. is selected • In U clauses other literals can be selected. • Significant performance enhancement.

22. UR Resolution Example • Given the sequence({s(a), p(b) }, {t(a), q(b)}) • and the clause{p(X), q(X), r(X)} • create the sequence • ({s(a), p(b)}, {t(a), q(b)}, {p(b), q(b), r(b)} ) X  b

23. Filtering Example • Given the sequence ({s(a), p(b)}, {t(a), q(b)}) • and the clause {p(X), q(X)} • create the sequence • ({s(a), p(b)}, {t(a), q(b)}, {p(b), q(b)} ) X  b

24. Case Analysis Example • Given the sequence ({s(a), p(b)}, {t(a), q(b)}) • and the clause {q(X), r(X), s(X)} • create the sequence • ({s(a), p(b)}, {t(a), q(b)}, {q(b), r(b), s(b)} ) X  b

25. Example Proof Using U Rules • All positive semantics • Clauses: • A1. XY, YX, X=Y A2. ZX, XY, ZY A3. g(X,Y)X, XY A4. g(X,Y)Y, XY A5. ZX, ZX  Y A6. ZY, ZX  Y A7. ZX  Y, ZX, ZY T. A  B = B  A

26. Example Proof Using U Rules • 1. {A  B = B  A} (T) • 2. {A  B  B  A, B  A  A  B, A  B = B  A} (Case Analysis, A1) • 3. {g(A  B, B  A)  B  A, A  B  B  A} (UR resolution, A4) • 4. {g(A  B, B  A)  B  A, g(…)  B} (UR resolution, A5) • 5. {g(A  B, B  A)  B  A, g(…)  A} (UR resolution, A6) • 6. {g(…)  B, g(…)  A, g(…)  A B} (UR resolution, A7) • 7. {A  B  B  A, g(…)  A B} (Filtering, A3)

27. Example Proof Using U Rules • 1. {A  B = B  A} • 2. {A  B  B  A, B  A  A  B, A  B = B  A} (Case Analysis) • 3. {g(A  B, B  A)  B  A, A  B  B  A} (UR resolution) • 4. {g(A  B, B  A)  B  A, g(…)  B} (UR resolution) • 5. {g(A  B, B  A)  B  A, g(…)  A} (UR resolution) • 8. {g(…)  B, g(…)  A, A  B  B  A,} (Resolution of 6. and 7.)

28. Example Proof Using U Rules • 1. {A  B = B  A} • 2. {A  B  B  A, B  A  A  B, A  B = B  A} (Case Analysis) • 3. {g(A  B, B  A)  B  A, A  B  B  A} (UR resolution) • 4. {g(A  B, B  A)  B  A, g(…)  B} (UR resolution) • 9. {g(A  B, B  A)  B  A, g(…)  B, A  B  B  A} (Resolution of 8. and 5.)

29. Example Proof Using U Rules • 1. {A  B = B  A} • 2. {A  B  B  A, B  A  A  B, A  B = B  A} (Case Analysis) • 3. {g(A  B, B  A)  B  A, A  B  B  A} (UR resolution) • 10. {g(A  B, B  A)  B  A} (Resolution of 9. and 4.)

30. Example Proof Using U Rules • 1. {A  B = B  A} • 2. {A  B  B  A, B  A  A  B, A  B = B  A} (Case Analysis) • 11. {A  B  B  A} (Resolution of 10. and 3.)

31. Example Proof Using U Rules • 1. {A  B = B  A} • 12. {B  A  A  B, A  B = B  A} (Resolution of 11 and 2) Now the other half of the proof will be done. Note that there is only one ascending sequence of clauses constructed by OSHL and we are only indicating part of it.

32. Implementation Results • Slower implementation speed of OSHL • Uniform strategy versus strategy selection • The choice of Otter • Influence of U rules on an earlier version: • None: 233 proofs in 30 seconds on TPTP problems • Using them: 900 proofs in 30 seconds • All results for trivial semantics

33. Heuristics • Delta size of propositional instances • Relevance distance • Favor propositional terms in the theorem

34. Problems Involving Definitions • Consider the problem • S1  S2  …  Sn = Sn  Sn-1  …  S1 •  is associated to the left • For increasing values of n, gives us a set of progressively harder problems • Requires expanding definitions of set union, set equality, and subset relations • Tested on OSHL-U, Otter and 3 other leading provers • Vampire • E-Setheo • DCTP

35. Problems Involving Definitions

36. Problems Involving Definitions

37. Problems Involving Definitions

38. Problems Involving Definitions • 9 other sets of problems tested • S1  S2  …  Sn= Sn Sn-1 …  S1, left side left associated, right side right associated • S1  S2  …  Sn=S1  S2  …  Sn S1  S2  …  Sn, both sides associated to the left • S1  S2  …  Sn=S1  S2  …  Sn S1  S2  …  Sn, left side left associated, right side right associated • S1 S2  …  Sn=S1  S2  …  Sn, left side left associated, right side right associated • 5 similar sets of problems involving  Results were similar for all 10 sets of problems tested

39. Implementation Results • OSHL has no special data structures. • Implemented in OCaML • No special equality methods • Semantics was implemented but frequently only trivial semantics was used. • Thus significant performance improvements are possible.