Skip to main content
Log in

Persistent and Quasi-Persistent Lemmas in Propositional Model Elimination

  • Published:
Annals of Mathematics and Artificial Intelligence Aims and scope Submit manuscript

Abstract

Model elimination is a back-chaining strategy to search for and construct resolution refutations. Recent extensions to model elimination, implemented in Modoc, have made it a practical tool for satisfiability checking, particularly for problems with known goals. Many formulas can be refuted more succinctly by recording certain derived clauses, called lemmas. Lemmas can be used where a clause of the original formula would normally be required. However, recording too many lemmas overwhelms the proof search. Lemma management has a significant effect on the performance of Modoc. Earlier research studied pure persistent (global) strategies, and pure unit-lemma (local) strategies. This paper describes and evaluates a hybrid strategy to control the lifetime of lemmas, as well as a new technique for deriving certain lemmas efficiently, using a lazy strategy. Unit lemmas are recorded locally as in previous practice, but certain lemmas that are considered valuable are asserted globally. A range of functions for estimating value is studied experimentally. Criteria are reported that appear to be suitable for a wide range of application-derived formulas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. O.L. Astrachan and D.W. Loveland, The use of lemmas in the model elimination procedure, Journal of Automated Reasoning 19 (1997) 117–141.

    Google Scholar 

  2. R.J. Bayardo, Jr. and R.C. Schrag, Using CSP look-back techniques to solve real-world SAT instances, in: Proceedings of 14th National Conference on Artificial Intelligence (AAAI-97) (1997) pp. 203–208.

  3. A. Biere, A. Cimatti, E.M. Clarke and M. Fujita, Symbolic model checking using SAT procedures instead of BDDs, in: Proceedings of Design Automation Conference (1999).

  4. M. Davis, G. Logemann and D. Loveland, A machine program for theorem-proving, Communications of the ACM 5 (1962) 394–397.

    Google Scholar 

  5. M. Davis and H. Putnam, A computing procedure for quantification theory, Journal of the Association for Computing Machinery 7 (1960) 201–215.

    Google Scholar 

  6. M.D. Ernst, T.D. Millstein and D.S. Weld, Automatic SAT-compilation of planning problems, in: Proc. of 15th International Joint Conference on Artificial Intelligence (1997) pp. 1169–1176.

  7. R.E. Fikes and N.J. Nilsson, STRIPS: A new approach to the application of theorem proving to problem solving, Artificial Intelligence 2(3/4) (1971) 189–208.

    Google Scholar 

  8. S. Fleisig, D.W. Loveland, A.K. Smiley and D.L. Yarmush, An implementation of the model elimination proof procedure, Journal of the Association for Computing Machinery 21(1) (1974) 124–139.

    Google Scholar 

  9. J.F. Groote and J.P. Warners, The propositional formula checker HeerHugo, Journal of Automated Reasoning 24(1) (2000) 101–125.

    Google Scholar 

  10. H. Kautz and B. Selman, Pushing the envelope: Planning, propositional logic, and stochastic search, in: Proc. of 13th National Conference on Artificial Intelligence (1996) pp. 1194–1201.

  11. T. Larrabee, Test pattern generation using Boolean satisfiability, IEEE Transactions on Computer-Aided Design 11(1) (1992) 6–22.

    Google Scholar 

  12. T. Larrabee and Y. Tsuji, Evidence for a satisfiability threshold for random 3CNF formulas, Technical Report UCSC-CRL-92-42, UC Santa Cruz, Santa Cruz, CA (1992).

  13. S.-J. Lee and D.A. Plaisted, Eliminating duplication with the hyper-linking strategy, Journal of Automated Reasoning 9(1) (1992) 25–42.

    Google Scholar 

  14. R. Letz, K. Mayr and C. Goller, Controlled integration of the cut rule into connection tableau calculi, Journal of Automated Reasoning 13(3) (1994) 297–337.

    Google Scholar 

  15. C.M. Li and Anbulagan, Heuristics based on unit propagation for satisfiability problem, in: Proceedings of International Joint Conference on Artificial Intelligence (1997) pp. 366–371.

  16. D.W. Loveland, Mechanical theorem-proving by model elimination, Journal of the Association for Computing Machinery 15(2) (1968) 236–251.

    Google Scholar 

  17. D.W. Loveland, A simplified format for the model elimination theorem-proving procedure, Journal of the Association for Computing Machinery 16(3) (1969) 349–363.

    Google Scholar 

  18. J. Minker and G. Zanon, An extension to linear resolution with selection function, Information Processing Letters 14(3) (1982) 191–194.

    Google Scholar 

  19. D. Mitchell, B. Selman and H. Levesque, Hard and easy distributions of SAT problems, in: Proceedings of 10th National Conference on Artificial Intelligence (AAAI-92), San Jose, CA (1992) pp. 459–465.

  20. B. Monien and E. Speckenmeyer, Solving satisfiability in less than 2n steps, Discrete Applied Mathematics 10 (1985) 287–295.

    Google Scholar 

  21. F. Okushi, Propositional theorem proving: Advanced lemma strategies and multi-agent search, Ph.D. Thesis, University of California, Santa Cruz (1998).

    Google Scholar 

  22. F. Okushi, Parallel cooperative propositional theorem proving, Annals of Mathematics and Artificial Intelligence 26(1-4) (1999) 59–85.

    Google Scholar 

  23. D.A. Plaisted, The search efficiency of theorem proving strategies, in: Proc. of 12th International Conference on Automated Deduction (1994) pp. 57–71.

  24. R.E. Shostak, Refutation graphs, Artificial Intelligence 7(1) (1976) 51–64.

    Google Scholar 

  25. J.P. Silva and K.A. Sakallah, GRASP-A new search algorithm for satisfiability, in: Proceedings of IEEE/ACM International Conference on Computer-Aided Design (1996) pp. 220–227.

  26. A. Van Gelder, Autarky pruning in propositional model elimination reduces failure redundancy, Journal of Automated Reasoning 23(2) (1999) 137–193.

    Google Scholar 

  27. A. Van Gelder, Complexity analysis of propositional resolution with autarky pruning, Discrete Applied Mathematics 96-97 (1999) 195–221.

    Google Scholar 

  28. A. Van Gelder and F. Okushi, Lemma and cut strategies for propositional model elimination, Annals of Mathematics and Artificial Intelligence 26(1-4) (1999) 113–132.

    Google Scholar 

  29. A. Van Gelder and F. Okushi, A propositional theorem prover to solve planning and other problems, Annals of Mathematics and Artificial Intelligence 26(1-4) (1999) 87–112.

    Google Scholar 

  30. H. Zhang, SATO: An efficient propositional prover, in: Proc. of 14th International Conference on Automated Deduction (1997) pp. 272–275.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Okushi, F., Van Gelder, A. Persistent and Quasi-Persistent Lemmas in Propositional Model Elimination. Annals of Mathematics and Artificial Intelligence 40, 373–401 (2004). https://doi.org/10.1023/B:AMAI.0000012873.60775.ba

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/B:AMAI.0000012873.60775.ba

Navigation