Advertisement

A Similarity Evaluation Technique for Cooperative Problem Solving with a Group of Agents

  • Seppo Puuronen
  • Vagan Terziyan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1652)

Abstract

Evaluation of distance or similarity is very important in cooperative problem solving with a group of agents. Distance between problems is used by agents to recognize nearest solved problems for a new problem, distance between solutions is necessary to compare and evaluate the solutions made by different agents, and distance between agents is useful to evaluate weights of the agents to be able to integrate them by weighted voting. The goal of this paper is to develop a similarity evaluation technique to be used for cooperative problem solving with a group of agents. Virtual training environment used for this goal is represented by predicates that define relationships within three sets: problems, solutions, and agents. We derive and interpret both internal and external relations between the pairs of subsets taken of the three sets: problems, solutions, and agents. The refinement technique presented is based on the derivation of the most supported solution of the group of agents and refining it further using a multilevel structure of agents.

Keywords

Weighted Vote External Relation Multilevel Structure Total Support Cooperative Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Reference

  1. 1.
    Bauer, E., Kohavi, R.: An Empirical Comparison of Voting Solution Algorithms: Bagging, Boosting, and Variants. Machine Learning, Vol. 33 (1998).Google Scholar
  2. 2.
    Cost, S., Salzberg, S.: A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features. Machine Learning, Vol. 10, No. 1 (1993) 57–78.Google Scholar
  3. 3.
    Dietterich, T.G.: Machine Learning Research: Four Current Directions. AI Magazine, Vol. 18, No. 4 (1997) 97–136.Google Scholar
  4. 4.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Cooperative problem solving. AAAI/ MIT Press (1997).Google Scholar
  5. 5.
    Jorgensen P.: P Jorgensen’s COM515 Project Page (1996) Available in WWW: http://jorg2.cit.bufallo.edu/COM515/~project.html.
  6. 6.
    Kohavi, R.: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: Proceedings of IJCAI’95 (1995).Google Scholar
  7. 7.
    Koppel, M., Engelson, S.P.: Integrating Multiple Agents by Finding their Areas of Expertise. In: AAAI-96 Workshop On Integrating Multiple Learning Models (1996) 53–58.Google Scholar
  8. 8.
    Merz, C.: Dynamical Selection of Learning Algorithms. In: D. Fisher, H.-J Lenz (Eds.), Learning from Data, Artificial Intelligence and Statistics, Springer Verlag, NY (1996).Google Scholar
  9. 9.
    Puuronen, S., Terziyan, V.: The Voting-type Technique in the Refinement of Multiple Expert Knowledge. In: Sprague, R.H., (Ed.), Proceedings of the Thirtieth Hawaii International Conference on System Sciences, Vol. V, IEEE Computer Society Press (1997) 287–296.Google Scholar
  10. 10.
    Rada R., Mili H., Bicknell E., Blettner M.: Development and application of a metric on semantic nets, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 19, No. 1 (1989) 17–30.Google Scholar
  11. 11.
    Rocha L., Luis M.: Fuzzification of Conversation Theory, In: F. Heylighen (ed.), Principia Cybernetica Conference, Free University of Brussels (1991).Google Scholar
  12. 12.
    Skalak, D.B.: Combining Nearest Neighbor Agents. Ph.D. Thesis, Dept. of Computer Science, University of Massachusetts, Amherst, MA (1997).Google Scholar
  13. 13.
    Tailor C., Tudhope D., Semantic Closeness and Solution Schema Based Hypermedia Access, In: Proceedings of the 3-rd International Conference on Electronic Library and Visual Information Research (ELVIRA’96), Milton, Keynes (1996).Google Scholar
  14. 14.
    Terziyan, V., Tsymbal, A., Puuronen, S.: The Decision Support System for Telemedicine Based on Multiple Expertise. International Journal of Medical Informatics, Vol. 49, No. 2 (1998) 217–229.CrossRefGoogle Scholar
  15. 15.
    Terziyan, V., Tsymbal, A., Tkachuk, A., Puuronen, S.: Intelligent Medical Diagnostics System Based on Integration of Statistical Methods. In: Informatica Medica Slovenica, Journal of Slovenian Society of Medical Informatics,Vol.3, Ns. 1,2,3 (1996) 109–114.Google Scholar
  16. 16.
    Wilson D., Martinez T., Improved Heterogeneous Distance Functions, Journal of Artificial Intelligence Research, Vol. 6 (1997) 1–34.zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Seppo Puuronen
    • 1
  • Vagan Terziyan
    • 2
  1. 1.Department of Computer Science and Information SystemsUniversity of JyväskyläJyväskyläFinland
  2. 2.Kharkov State Technical University of RadioelectronicsKharkovUkraine

Personalised recommendations