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Logical inference and polyhedral projection

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Computer Science Logic (CSL 1991)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 626))

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Abstract

We explore connections between polyhedral projection and inference in propositional logic. We formulate the problem of drawing all inferences that contain a restricted set of atoms (i.e., all inferences that pertain to a given question) as a logical projection problem. We show that polyhedral projection partially solves this problem and in particular derives precisely those inferences that can be obtained by a certain form of unit resolution. We prove that this unit resolution algorithm is exponential in the number of atoms in the restricted set but is polynomial in the problem size when this number of fixed. We also survey a number of new satisfiability algorithms that have been suggested by the polyhedral interpretation of propositional logic.

Supported in part by the Air Force Office of Scientific Research, Grant number AFOSR-91-0287.

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References

  1. Andersen, K. A., and J. N. Hooker, Bayesian logic, to appear in Decision Support Systems.

    Google Scholar 

  2. Arvind, V. and S. Biswas, An O(n 2) algorithm for the satisfiability problem of a subset of propositional sentences in CNF that includes all Horn sentences, Information Processing Letters 24 (1987) 67–69.

    Google Scholar 

  3. Billionnet, A., and A. Sutter, An efficient algorithm for the 3-satisfiability problem, Research Report 89-13, Centre d'études et de recherche en informatique, 292 rue Saint-Martin, 75141 Paris Cedex 03, 1989.

    Google Scholar 

  4. E. Boros, P. Hammer and J. N. Hooker, Boolean regression, working paper 1991-30, Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, PA 15213 USA.

    Google Scholar 

  5. E. Boros, P. L. Hammer, and J. N. Hooker, Predicting cause-effect relationships from incomplete discrete observations, working paper 1991-22, Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, PA 15213 USA.

    Google Scholar 

  6. Blair, C., R. G. Jeroslow, and J. K. Lowe, Some results and experiments in programming techniques for propositional logic, Computers and Operations Research 13 (1988) 633–645.

    Google Scholar 

  7. Chandru, V., and J. N. Hooker, Logical inference: A mathematical programming perspective, in S. T. Kumara, R. L. Kashyap, and A. L. Soyster, eds., Artificial Intelligence: Manufacturing Theory and Practice, Institute of Industrial Engineers (1988) 97–120.

    Google Scholar 

  8. Chandru, V., and J. N. Hooker, Extended Horn sets in propositional logic, Journal of the ACM 38 (1991) 203–221.

    Google Scholar 

  9. Chandru, V., and J. N. Hooker, Optimization Methods for Logical Inference, Wiley, to appear.

    Google Scholar 

  10. Chvátal, V., Edmonds polytopes and a hierarchy of combinatorial problems, Discrete Mathematics 4 (1973) 305–337.

    Google Scholar 

  11. Cook, S. A., The complexity of theorem-proving procedures, Proceedings of the Third Annual ACM Symposium on the Theory of Computing (1971) 151–158.

    Google Scholar 

  12. Dantzig, G. B., Linear Programming and Extensions, Princeton University Press (1963).

    Google Scholar 

  13. Davis, M., and H. Putnam, A computing procedure for quantification theory, Journal of the ACM 7 (1960) 201–215.

    Google Scholar 

  14. Dowling, W. F., and J. H. Gallier, Linear-time algorithms for testing the satisfiability of propositional Horn formulae, Journal of Logic Programming 1 (1984) 267–284.

    Google Scholar 

  15. G. Gallo and G. Rago, A hypergraph approach to logical inference for datalog formulae, working paper, Dip. di Informatica, University of Pisa, Italy (September 1990).

    Google Scholar 

  16. Gallo, G., and M. G. Scutella, Polynomially soluble satisfiability problems, Information Processing Letters 29 (1988) 221–227.

    Google Scholar 

  17. Gallo, G., and G. Urbani, Algorithms for testing the satisfiability of propositional formulae, Journal of Logic Programming 7 (1989) 45–61.

    Google Scholar 

  18. Genesereth, M. R., and N. J. Nilsson, Logical Foundations of Artificial Intelligence, Morgan Kaufmann (Los Altos, CA, 1987).

    Google Scholar 

  19. Glover, F., and H. J. Greenberg, Logical testing for rule-based management, Annals of Operations Research 12 (1988) 199–215.

    Google Scholar 

  20. Haken, A., The intractability of resolution, Theoretical Computer Science 39 (1985) 297–308.

    Google Scholar 

  21. Hansen, P., A cascade algorithm for the logical closure of a set of binary relations, Information Processing Letters 5 (1976) 50–55.

    Google Scholar 

  22. Hansen, P., B. Jaumard and M. Minoux, A linear expected-time algorithm for deriving all logical conclusions implied by a set of boolean inequalities, Mathematical Programming 34 (1986) 223–231.

    Google Scholar 

  23. Harche, F., J. N. Hooker and G. L. Thompson, A computational study of satisfiability algorithms for propositional logic, working paper 1991-27, Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, PA 15213 USA, 1991.

    Google Scholar 

  24. Hooker, J. N., Resolution vs. cutting plane solution of inference problems: Some omputational experience, Operations Research Letters 7 (1988) 1–7.

    Google Scholar 

  25. Hooker, J. N., A quantitative approach to logical inference, Decision Support Systems 4 (1988) 45–69.

    Google Scholar 

  26. Hooker, J. N., Resolution vs. cutting plane solution of inference problems: some computational experience, Operations Research Letters 7 (1988) 1–7.

    Google Scholar 

  27. Hooker, J. N., Generalized resolution and cutting planes, Annals of Operations Research 12 (1988) 217–239.

    Google Scholar 

  28. Hooker, J. N., Input proofs and rank one cutting planes, ORSA Journal on Computing 1 (1989) 137–145.

    Google Scholar 

  29. Hooker, J. N., Generalized resolution for 0–1 linear inequalities, to appear in Annals of Mathematics and AI.

    Google Scholar 

  30. Hooker, J. N., New methods for inference in first-order predicate logic, working paper 1991-11, Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, PA 15213 USA, 1991.

    Google Scholar 

  31. Hooker, J. N. and C. Fedjki, Branch-and-cut solution of inference problems in propositional logic, to appear in Annals of Mathematics and AI.

    Google Scholar 

  32. Hooker, J. N., and H. Yan, Verifying logic circuits by Benders decomposition, working paper 1991-29, Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, USA, August 1988.

    Google Scholar 

  33. Jaumard, B., P. Hansen and M. P. Aragaö, Column generation methods for probabilistic logic, to appear in ORSA Journal on Computing.

    Google Scholar 

  34. Jeroslow, R. E., Computation-oriented reductions of predicate to propositional logic, Decision Support Systems 4 (1988) 183–197.

    Google Scholar 

  35. Jeroslow, R. E., and J. Wang, Solving propositional satisfiability problems, Annals of Mathematics and AI 1 (1990) 167–187.

    Google Scholar 

  36. Kamath, A. P., N. K. Karmarkar, K. G. Ramakrishnan, and M. G. C. Resende, Computational experience with an interior point algorithm on the satisfiability problem, in R. Kannan and W. R. Pulleyblank, eds., Integer Programming and Combinatorial Optimization, University of Waterloo Press (Waterloo, Ont., 1990) 333–349.

    Google Scholar 

  37. Kamath, A. P., N. K. Karmarkar, K. G. Ramakrishnan, and M. G. C. Resende, A continuous aproach to inductive inference, manuscript, AT&T Bell Labs, Murray Hil, NJ 07974 USA, 1991.

    Google Scholar 

  38. Kavvadias, D., and C. H. Papadimitriou, A linear programming approach to reasoning about probabilities, to appear in Annals of Mathematics and Artificial Intelligence.

    Google Scholar 

  39. Karp, R. M., Reducibility among combinatorial problems, in R. E. Miller and J. W. Thatcher, eds., Complexity of Computer Computations, Plenum Press (1972) 85–103.

    Google Scholar 

  40. Loveland, D. W., Automated Theorem Proving: A Logical Basis, North-Holland (1978).

    Google Scholar 

  41. Mitterreiter, I., and F. J. Radermacher, Experiments on the running time behavior of some algorithms solving propositional logic problems, working paper, Forschungsinstitut für anwendungsorientierte Wissensverarbeitung, Ulm, Germany (1991).

    Google Scholar 

  42. Patrizi, G., The equivalence of an LCP to a parametric linear program with a scalar parameter, to appear in European Journal of Operational Research.

    Google Scholar 

  43. Quine, W. V., The problem of simplifying truth functions, American Mathematical Monthly 59 (1952) 521–531.

    Google Scholar 

  44. Quine, W. V., A way to simplify truth functions, American Mathematical Monthly 62 (1955) 627–631.

    Google Scholar 

  45. Robinson, J. A., A machine-oriented logic based on the resolution principle, Journal of the ACM 12 (1965) 23–41.

    Google Scholar 

  46. Spera, C., Computational results for solving large general satisfiability problems, technical report, Centro di Calcolo Elettronico, Università degli Studi di Siena, Italy, 1990.

    Google Scholar 

  47. Triantaphylou, E., A. L. Soyster, and S. R. T. Kumara, Generating logical expressions from positive and negative examples via a branch-and-bound approach, manuscript, Industrial and Management Systems Engineering, Pennsylvania State University, University Park, PA 16802 USA, 1991.

    Google Scholar 

  48. Truemper, K., Polynomial theorem proving: I. Central matrices, technical report UTDCS-34-90, Computer Science Dept., University of Texas at Dallas, Richardson, TX 75083-0688 USA (1990).

    Google Scholar 

  49. Truemper, K., and F. J. Radermacher, Analyse der Leistungsfähigkeit eines neuen Systems zur Auswertung aussagenlogisher Probleme, technical report FAW-TR-90003, Forschungsinstitut für anwendungsorientierte Wissensverarbeitung, Ulm, Germany (1990).

    Google Scholar 

  50. Tseitin, G. S., On the complexity of derivations in the propositional calculus, in A. O. Slisenko, ed., Structures in Constructive Mathematics and Mathematical Logic, Part II (translated from Russian, 1968) 115–125.

    Google Scholar 

  51. Wang, J., and J. Vande Vate, Question-asking strategies for Horn clause systems, working paper, Georgia Institute of Technology, Atlanta, GA, 1989.

    Google Scholar 

  52. Williams, H. P., Fourier-Motzkin elimination extension to integer programming problems, Journal of Combinatorial Theory 21 (1976) 118–123.

    Google Scholar 

  53. Williams, H. P., Model Building in Mathematical Programming, Wiley (1985).

    Google Scholar 

  54. Williams, H. P., Linear and integer programming applied to the propositional calculus, International Journal of Systems Research and Information Science 2 (1987) 81–100.

    Google Scholar 

  55. Yamasaki, S. and S. Doshita, The satisfiability problem for a class consisting of Horn sentences and some non-Horn sentences in propositional logic, Information and Control 59 (1983) 1–12.

    Google Scholar 

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Egon Börger Gerhard Jäger Hans Kleine Büning Michael M. Richter

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© 1992 Springer-Verlag Berlin Heidelberg

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Hooker, J.N. (1992). Logical inference and polyhedral projection. In: Börger, E., Jäger, G., Kleine Büning, H., Richter, M.M. (eds) Computer Science Logic. CSL 1991. Lecture Notes in Computer Science, vol 626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0023767

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  • DOI: https://doi.org/10.1007/BFb0023767

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  • Print ISBN: 978-3-540-55789-0

  • Online ISBN: 978-3-540-47285-8

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