Implementing an automated reasoning system for multi-agent knowledge and time

  • Lifeng He
  • Yuyan Chaot
  • Shohey Kato
  • Tetsuo Araki
  • Hirohisa Seki
  • Hidenori Itoh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1286)


We present an implementation of an automated reasoning system for multi-agent knowledge and time, which can be used to describe multi-agent environments. Our reasoning procedure is based on a so-called semantic method. That is, suppose that a multi-agent environment is given by a multi-agent knowledge and time modal logic formula set U. To see whether a multi-agent knowledge and time modal logic formula F can be derived from U, all formulas in U and the negation of F are first translated, according to the possible worlds semantics, into a semantically equivalent first-order formula set, from which we then derive a set of first-order clauses. Thus, F logically follows from U iff there is a refutation in the translated clause set. Since we have to use some transitive axioms and deal with inequalities when reasoning about the set of translated first-order clauses, we augment a general purpose first-order theorem proof procedure ME (the Model Elimination) [11] with the capabilities of using transitive axioms and dealing with inequalities. Theory resolution is incorporated into our reasoning procedure for using transitive axioms efficiently. We present our implementation and show some experimental results.


multi-agent environment system implementation automated reasoning knowledge and time 


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  1. 1.
    R. Anderson and W.W. Bledsoe, ‘A Linear format for resolution with merging and a new technique for establishing completeness', J. ACM 28, pp 193–214, 1970.Google Scholar
  2. 2.
    J. Akahani, ‘A Multi-Agent System Simulator Based on Modal Logic', Multi-Agent and Cooperative Computation'91, pp.49–57, Modern Publisher, Japan, 1991.Google Scholar
  3. 3.
    L. Catach, ‘TABLEAUX: A General Theorem Prover for Modal Logic', Journal of Automated Reasoning 7: pp.489–510, 1991.Google Scholar
  4. 4.
    C.L. Chang and R.C.T. Lee, 'symbolic Logic and Mechanical Theorem Proving', Academic Press, 1973.Google Scholar
  5. 5.
    J. Elgot-Drapkin, 'step-Logic and the Three-wise-men Problem', Proceedings of AAAI, pp.412–417, 1991.Google Scholar
  6. 6.
    P. J. Gmytrsiewicz and E.H. Durfee, ‘Elements of a Utilitarian Theory of Knowledge and Action', Proceedings of IJCAI, pp.396–402, 1993.Google Scholar
  7. 7.
    G.E. Hughes and M.J. Cresswell, ‘An Introduction to Modal Logic', Methuen, London, 1968.Google Scholar
  8. 8.
    J.Y. Halpern and M.Y. Vardi, ‘The Complexity of Reasoning About Knowledge and Time: Extended Abstract', Proceedings of the Eighteenth Annual ACM Symposium on Theory of Computing, pp.304–315, 1986.Google Scholar
  9. 9.
    S. Kraus and J. Wilkenfeld, ‘Negotiations over Time in a Multi Agent Environment: Preliminary Report', pp.56–61, Proceedings of IJCAI, 1993.Google Scholar
  10. 10.
    R. Manthey and F. Bry, 'sATCHMO: a theorem prover implemented in Prolog', Proceedings of 9th Conference on Automated Deduction, 1988.Google Scholar
  11. 11.
    D.W. Loveland, ‘Mechanical Theorem Proving by Model Elimination', J. of the ACM 15, pp.236–251, 1968.Google Scholar
  12. 12.
    F. Lin and Y. Shoham, ‘On the Persistence of Knowledge and Ignorance: A Preliminary report', In the fourth Int. Workshop on Nonmonotonic Reasoning, 1992.Google Scholar
  13. 13.
    C. Morgan, ‘Methods for Automated Theorem Proving in Nonclassical Logics', IEEE, Transactions on Computers, vol.c-25, No.8, August 1976.Google Scholar
  14. 14.
    U.W. Schwuttke and A.G. Quan, ‘Enhancing Performance of Cooperating Agents in Real-Time Diagnostic System', Proceedings of IJCAI, pp.332–337, 1993.Google Scholar
  15. 15.
    M. Stickel, “Automated Deduction by Theory Reasoning', Journal of Automated Reasoning, 1(4):333–356, 1985.Google Scholar
  16. 16.
    X. Wang and H. Chen, ‘On Assumption Reasoning in Multi-Reasoner System', Proceedings of PRICAI, pp.381–383, 1990.Google Scholar
  17. 17.
    G. Weiß, ‘Learning to Coordinate Action in Multi-Agent System', Proceedings of IJCAI, pp.311–316, 1993.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Lifeng He
    • 1
  • Yuyan Chaot
    • 2
  • Shohey Kato
    • 1
  • Tetsuo Araki
    • 3
  • Hirohisa Seki
    • 1
  • Hidenori Itoh
    • 1
  1. 1.Nagoya Institute of TechnologyNagoyaJapan
  2. 2.Nagoya UniversityNagoyaJapan
  3. 3.Fukui UniversityFukuiJapan

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