Analyzing Conflicts with Concept-Based Learning

  • Boris A. Galitsky
  • Sergei O. Kuznetsov
  • Mikhail V. Samokhin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3596)

Abstract

A machine learning technique for handling scenarios of interaction between conflicting agents is suggested. Scenarios are represented by directed graphs with labeled vertices (for mental actions) and arcs (for temporal and causal relationships between these actions and their parameters). The relation between mental actions and their descriptions gives rise to a concept lattice. Classification of an undetermined scenario is realized by comparing partial matchings of its graph with graphs of positive and negative examples. Developed scenario representation and comparative analysis techniques are applied to the classification of textual customer complaints.

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References

  1. 1.
    Bratman, M.E.: Intention, plans and practical reason. Harvard University Press (1987)Google Scholar
  2. 2.
    Fagin, R., Halpern, J.Y., Moses, Y., Vardi, M.Y.: Reasoning about knowledge. MIT Press, Cambridge (1995)MATHGoogle Scholar
  3. 3.
    Finn, V.K.: Plausible Reasoning in Systems of JSM-type, Itogi Nauki I Techniki. Seriya Iformatika 15, 54–101 (1991) (in Russian)Google Scholar
  4. 4.
    Galitsky, B.: A Library of Behaviors: Implementing Commonsense Reasoning about Mental World. In: 8th Intl. Conf. on Knowledge-Based Intelligent Info Syst. (2004)Google Scholar
  5. 5.
    Galitsky, B.: Natural Language Question Answering System: Technique of Semantic Headers. Advanced Knowledge International, Adelaide, Australia (2003)Google Scholar
  6. 6.
    Galitsky, B., Tumarkina, I.: Justification of Customer Complaints using Emotional States and Mental Actions. In: FLAIRS, Miami, FL (2004)Google Scholar
  7. 7.
    Ganter, B., Kuznetsov, S.O.: Pattern Structures and Their Projections. In: Delugach, H.S., Stumme, G. (eds.) ICCS 2001. LNCS (LNAI), vol. 2120, pp. 129–142. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    Ganter, B., Wille, R.: Formal Concept Analysis. Mathematical Foundations. Springer, Heidelberg (1999)MATHGoogle Scholar
  9. 9.
    Guerra-Hernández, A., Fallah-Seghrouchni, A.E., Soldano, H.: Learning in BDI multi-agent systems. In: Dix, J., Leite, J. (eds.) CLIMA 2004. LNCS (LNAI), vol. 3259, pp. 218–233. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  11. 11.
    Kuznetsov, S.O.: Learning of Simple Conceptual Graphs from Positive and Negative Examples. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 384–391. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  12. 12.
    Laza, R., Corchado, J.M.: CBR-BDI Agents in Planning. In: Symposium on Informatics and Telecommunications (SIT 2002), Sevilla, Spain, September 25-27, pp. 181–192 (2002)Google Scholar
  13. 13.
    Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)MATHGoogle Scholar
  14. 14.
    Muller, H.J., Dieng, R. (eds.): Computational Conflicts: Conflict Modeling for Distributed Intelligent Systems. Springer, New York (2000)Google Scholar
  15. 15.
    Olivia, C., Chang, C.F., Enguix, C.F., Ghose, A.K.: Case-Based BDI Agents: an Effective Approach for Intelligent Search on the World Wide Web. In: Intelligent Agents in Cyberspace. AAAI Spring Symposium (1999)Google Scholar
  16. 16.
    Shanahan, M.: Solving the frame problem. MIT Press, Cambridge (1997)Google Scholar
  17. 17.
    Sowa, J.: Conceptual Graphs, Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, Reading (1984)Google Scholar
  18. 18.
    Stone, P., Veloso, M.: Multiagent Systems: A Survey from a Machine Learning Perspective. Autonomous Robotics 8(3), 345–383 (2000)CrossRefGoogle Scholar
  19. 19.
    Weiss, G., Sen, S.: Adaptation and Learning in Multiagent Systems. LNCS (LNAI), vol. 1042. Springer, Heidelberg (1996)Google Scholar
  20. 20.
    Wooldridge, M.: Reasoning about Rational Agents. The MIT Press, Cambridge (2000)MATHGoogle Scholar
  21. 21.
    Yevtushenko, S.A.: Last accessed April 7 (2005), http://www.sf.net/projects/conexp
  22. 22.
    Cohen, P.R., Levesque, H.J.: Performatives in a Rationally Based Speech Act Theory. In: Proceedings of the 28th conference on Association for Computational Linguistics, pp. 79–88 (1990)Google Scholar
  23. 23.
    Searle, J.: Speech Acts: An Essay in the Philosophy of Language, Cambridge, Eng. Cambridge University Press, Cambridge (1969)Google Scholar
  24. 24.
    Bach, K., Harnish, R.M.: Linguistic Commuication and Speech Acts. MIT Press, Cambridge (1979)Google Scholar
  25. 25.
    Searle, J.: Expression and Meaning: Studies in the Theory of Speech Acts. Cambridge University Press, Cambridge (1979)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Boris A. Galitsky
    • 1
  • Sergei O. Kuznetsov
    • 2
  • Mikhail V. Samokhin
    • 2
  1. 1.School of Computer Science and Information Systems, Birkbeck CollegeUniversity of LondonLondonUK
  2. 2.All-Russian Institute for Scientific and Technical Information (VINITI)MoscowRussia

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