Market-Inspired Approach to Collaborative Learning

  • Jan Tožička
  • Michal Jakob
  • Michal Pěchouček
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4149)


The paper describes a decentralized peer-to-peer multi-agent learning method based on inductive logic programming and knowledge trading. The method uses first-order logic for model representation. This enables flexible sharing of learned knowledge at different levels of abstraction as well as seamless integration of models created by other agents. A market-inspired mechanism involving knowledge trading is used for inter-agent coordination. This allows for decentralized coordination of learning activity without the need for a central control element. In addition, agents can participate in collaborative learning while pursuing their individual goals and maintaining full control over the disclosure of their private information. Several different types of agents differing in the level and form of knowledge exchange are considered. The mechanism is evaluated using a set of performance criteria on several scenarios in a realistic logistic domain extended with adversary behavior. The results show that using the proposed method agents can collaboratively learn properties of their environment, and consequently significantly improve their operation.


Collaborative Learn Multiagent System Inductive Logic Programming Transporter Agent Inductive Logic Programming System 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Foltyn, L., Toz̆ic̆ka, J., Rollo, M., Pĕchouc̆ek, M., Jisl, P.: Reflective-cognitive architecture: From abstract concept to self-adapting agent. In: DIS 2006: Proceedings of the Workshop on Distributed Intelligent Systems. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  2. 2.
    Kudenko, D., Kazakov, D., Alonso, E. (eds.): AAMAS 2004. LNCS (LNAI), vol. 3394. Springer, Heidelberg (2005)Google Scholar
  3. 3.
    Panait, L., Luke, S.: Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems 11(3), 387–434 (2005)CrossRefGoogle Scholar
  4. 4.
    Weiss, G., Dillenbourg, P.: What is ’multi’ in multi-agent learning? In: Dillenbourg, P. (ed.) Collaborative-learning: Cognitive and Computational Approaches, pp. 64–80. Elsevier, Oxford (1999)Google Scholar
  5. 5.
    Grecu, D.L., Becker, L.A.: Coactive Learning for Distributed Data Mining. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD 1998), New York, pp. 209–213 (1998)Google Scholar
  6. 6.
    Kazakov, D., Kudenko, D.: Machine learning and inductive logic programming for multi-agent systems. In: Luck, M., Mařík, V., Štěpánková, O., Trappl, R. (eds.) ACAI 2001 and EASSS 2001. LNCS (LNAI), vol. 2086, pp. 246–270. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. 7.
    Guerra-Hernandez, A., Fallah-Seghrouchni, A., Soldano, H.: Learning in BDI multi-agent systems. In: Proceedings of CLIMA 2003, pp. 185–200 (2004)Google Scholar
  8. 8.
    Alonso, E., d’Inverno, M., Kudenko, D., Luck, M., Noble, J.: Learning in multi-agent systems. Knowledge Engineering Review 16(3), 277–284 (2001)CrossRefGoogle Scholar
  9. 9.
    Wei, Y.Z., Moreau, L., Jennings, N.R.: Recommender systems: a market-based design. In: AAMAS 2003: Proceedings of the second international joint conference on Autonomous agents and multiagent systems, pp. 600–607. ACM Press, New York (2003)CrossRefGoogle Scholar
  10. 10.
    Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. A Bradford Book. MIT Press, Cambridge (2001)Google Scholar
  11. 11.
    Kargupta, H., Chan, P. (eds.): Advances in Distributed and Parallel Knowledge Discovery. MIT/AAAI Press (2000)Google Scholar
  12. 12.
    Park, B., Kargupta, H.: Distributed Data Mining: Algorithms, Systems, and Applications. In: Ye, N. (ed.) Data Mining Handbook. IEA, pp. 341–358 (2002)Google Scholar
  13. 13.
    Giannella, C., Bhargava, R., Kargupta, H.: Multi-agent systems and distributed data mining. In: Klusch, M., Ossowski, S., Kashyap, V., Unland, R. (eds.) CIA 2004. LNCS (LNAI), vol. 3191, pp. 1–15. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Klusch, M., Lodi, S., Moro, G.: Agent-based distributed data mining: The kdec scheme. In: Klusch, M., Bergamaschi, S., Edwards, P., Petta, P. (eds.) Intelligent Information Agents. LNCS, vol. 2586, pp. 104–122. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  15. 15.
    Muggleton, S., Raedt, L.D.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19/20, 629–679 (1994)CrossRefGoogle Scholar
  16. 16.
    van Rijsbergen, C.J.: Information Retrieval. Butterworths, London (1979)Google Scholar
  17. 17.
    S̆is̆lák, D., Rehák, M., Pĕchouc̆ek, M., Rollo, M., Pavlíček, D.: A-globe: Agent development platform with inaccessibility and mobility support. In: Unland, R., Klusch, M., Calisti, M. (eds.) Software Agent-Based Applications, Platforms and Development Kits, pp. 21–46. Birkhäuser Verlag, Basel (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jan Tožička
    • 1
  • Michal Jakob
    • 1
  • Michal Pěchouček
    • 1
  1. 1.Gerstner Laboratory, Department of CyberneticsCzech Technical UniversityPragueCzech Republic

Personalised recommendations