Skip to main content

Heuristics for Resolution in Propositional Logic

  • Conference paper
KI 2009: Advances in Artificial Intelligence (KI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5803))

Included in the following conference series:

  • 1706 Accesses

Abstract

One of the reasons for the efficiency of automated theorem systems is the usage of good heuristics. There are different semantic heuristics such as set of support which make use of additional knowledge about the problem at hand. Other widely employed heuristics work well without making any additional assumptions. A heuristic which seems to be generally useful is to “keep things simple” such as prefer small clause sets over big ones. For the simple case of propositional logic with three variables, we will look at this heuristic and compare it to a heuristic which takes the structure of the clause set into consideration. In the study we will take into account the class of all possible problems.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Ben-Sasson, E., Wigderson, A.: Short proofs are narrow–resolution made simple. Journal of the ACM 48(2), 149–169 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  2. Frisch, A.M., Jefferson, C., Hernandez, B.M., Miguel, I.: The rules of constraint modelling. In: Proc. of the 19th IJCAI, pp. 109–116 (2005)

    Google Scholar 

  3. Kerber, M.: Normalization issues in mathematical representations. In: Autexier, S., Campbell, J., Rubio, J., Sorge, V., Suzuki, M., Wiedijk, F. (eds.) AISC 2008, Calculemus 2008, and MKM 2008. LNCS (LNAI), vol. 5144, pp. 494–503. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  4. Kovacs, T., Kerber, M.: A study of structural and parametric learning in XCS. Evolutionary Computation Journal 14(1), 1–19 (2006)

    Article  Google Scholar 

  5. Pearl, J.: Heuristics – Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, Reading (1984)

    Google Scholar 

  6. Chang, C.L., Lee, R.C.T.: Symbolic Logic and Mechanical Theorem Proving. Academic Press, New York (1973)

    MATH  Google Scholar 

  7. Sutcliffe, G.: The TPTP Problem Library and Associated Infrastructure. The FOF and CNF Parts, v3.5.0. Journal of Automated Reasoning (to appear, 2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kerber, M. (2009). Heuristics for Resolution in Propositional Logic. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_82

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04617-9_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04616-2

  • Online ISBN: 978-3-642-04617-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics