Skip to main content
SpringerLink
Log in

Learning collaboration strategies for committees of learning agents

  • Published:
Autonomous Agents and Multi-Agent Systems Aims and scope Submit manuscript

Abstract

A main issue in cooperation in multi-agent systems is how an agent decides in which situations is better to cooperate with other agents, and with which agents does the agent cooperate. Specifically in this paper we focus on multi-agent systems composed of learning agents, where the goal of the agents is to achieve a high accuracy on predicting the correct solution of the problems they encounter. For that purpose, when encountering a new problem each agent has to decide whether to solve it individually or to ask other agents for collaboration. We will see that learning agents can collaborate forming committees in order to improve performance. Moreover, in this paper we will present a proactive learning approach that will allow the agents to learn when to convene a committee and with which agents to invite to join the committee. Our experiments show that learning results in smaller committees while maintaining (and sometimes improving) the problem solving accuracy than forming committees composed of all agents.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Aamodt A. and Plaza E. (1994). Case-based reasoning: Foundational issues, methodological variations and system approaches. Artificial Intelligence Communications 7(1): 39–59. online at < url:http://www.iiia.csic.es/People/enric/AICom_ToC.html >.

    Google Scholar 

  2. Aha, D. (Ed.) (1997) Lazy learning. Kluwer Academic Publishers.

  3. Brams S.J. and Fishburn P.C. (1983). Approval voting. Birkhauser, Boston

    MATH  Google Scholar 

  4. Breiman L. (1996). Bagging predictors. Machine Learning 24(2): 123–140

    MATH  MathSciNet  Google Scholar 

  5. Cestnik, B., & Bratko, I. (1991). On estimating probabilities in tree pruning. In Machine learning-European working session on learning-91, Vol. 482 of Lecture Notes in Artificial Intelligence. (pp. 151–163). Springer Verlag.

  6. Chan, P. K., & Stolfo, S. J. (1995). A comparative evaluation of voting and meta-learning on partitioned data. In Proceedings of the 12th international conference on Machine learning, pp. 90–98.

  7. Cover T. and Hart P. (1967). Nearest neighbor pattern classification’. IEEE Transactions on Information Theory 13(1): 21–27

    Article  MATH  Google Scholar 

  8. Dignum F., Dunin-Keplicz B. and Verbrugge R. (2001). Agent theory for eam formation by dialogue. Lecture Notes in Computer Science 1986: 150–166

    Article  MATH  Google Scholar 

  9. Dutta, P. S., & Sen, S. (2002). Emergence of stable coalitions via task exchanges. In C. Castelfranchi, & W. L. Johnson (Eds.), Proceedings of 1st international conference on automous agents and multiagent systems, pp. 312–313.

  10. Esteva, M., Padget, J., & Sierra, C., (To appear). Formalising a language for institutions and norms. In Intelligent agents VIII, proceedings ATAL’01.

  11. Esteva, M., Rodriguez-Aguilar, J. A., Sierra, C., Garcia, P., & Arcos, J. L. (2001). On the formal specification of electronic institutions. In Agent mediated electronic commerce, Vol. 1991 of LNAI. Springer-Verlag.

  12. Freund, Y., & Schapire, R. E. (1996). Experiments with a new Boosting algorithm. In Proceedings 13th international conference on machine learning, pp. 148–146.

  13. Gama, J. (1998). Local cascade generalization. In Proceedings 15th international conference on machine learning, pp. 206–214.

  14. Gomez, M., & Plaza, E. (2004). Extending matchmaking to maximize capability reuse. In Proceedings of the AAMAS 2004, pp. 144–152.

  15. Hansen L.K. and Salamon P. (1990). Neural networks ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12: 993–1001

    Article  Google Scholar 

  16. Leake, D. B. & Sooriamurthi, R. (2001). When two case bases are better than one: Exploiting multiple case bases. In ICCBR, pp. 321–335.

  17. Leake, D. B. & Sooriamurthi, R. (2002). Managing multiple case bases: Dimensions and issues. In Proceedings of the 15th international Florida Artificial Intelligence Research Society (FLAIRS), pp. 106–110.

  18. McGinty, L., & Smyth, B. (2001). Collaborative case-based reasoning: Applications in personalized route planning. In Case based reasoning ICCBR-01, pp. 362–376.

  19. Ontañón, S., & Plaza, E. (2002a). A bartering aproach to improve multiagent learning. In 1st International joint conference in autonomous agents and multiagent systems.

  20. Ontañón S. and Plaza E. (2002b). Collaboration strategies to improve multiagent learning. Lecture Notes in Artificial Intelligence 2430: 331–344

    Google Scholar 

  21. Ontañón S. and Plaza E. (2003a). Collaborative case retention strategies for CBR agents. Lecture Notes in Artificial Intelligence 2689: 392–406

    Google Scholar 

  22. Ontañón, S., & Plaza, E. (2003b). Justification-based multiagent learning. In International conference on machine learning ICML-2003, pp. 576–583.

  23. Ontañón, S. & Plaza, E. (2005). Recycling data for multi-agent learning. In Proceedings 22nd international conference on machine learning ICML-2005, pp. 633–640.

  24. Ontañón, S., & Plaza, E. (2006). Arguments and counterexamples in case-based joint deliberation. In Proceedings AAMAS’06 workshop on argumentation on multi-agent systems, p. to appear.

  25. Ontañón S., (2005). Ensemble case based learning for multi-agent systems. Ph.D. thesis, Universitat Autònoma de Barcelona.

  26. Perrone, M. P., & Cooper, L. N. (1993). When networks disagree: Ensemble methods for hybrid neural networks. In Artificial neural networks for speech and vision, Chapman-Hall.

  27. Plaza, E., & Ontañón, S. (2001). Ensemble case-based reasoning: Collaboration policies for multiagent cooperative CBR. In I. Watson, & Q. Yang (Eds.), In case-based reasoning research and development: ICCBR-2001, pp. 437–451.

  28. Plaza E. and Ontañón S. (2003). Cooperative multiagent learning. Lecture Notes in Artificial Intelligence 2636: 1–17

    MATH  Google Scholar 

  29. Quinlan J.R. (1986). Induction of decision trees. Machine Learning 1(1): 81–106

    Google Scholar 

  30. Wolpert, D. H. (1990). Stacked generalization. Technical Report LA-UR-90-3460, Los Alamos, NM.

  31. Wooldridge, M., & Jennings, N. R. (1994). Towards a theory of cooperative problem solving. In Proceedings modelling autonomous agents in a multi-agent world (MAAMAW-94). (pp. 15–26). Odense, Denmark.

Download references

Author information

Author notes

  1. Santiago Ontañón

    Present address: MAiA – Department of Applied Mathematics, University of Barcelona, Gran Via 585, 08007, Barcelona, Catalonia, Spain

Authors and Affiliations

  1. IIIA – Artificial Intelligence Research Institute, CSIC – Spanish Counsel for Scientific Research, Campus UAB, 08193, Bellaterra, Catalonia, Spain

    Enric Plaza & Santiago Ontañón

Authors
  1. Enric Plaza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Santiago Ontañón
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Santiago Ontañón.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Plaza, E., Ontañón, S. Learning collaboration strategies for committees of learning agents. Auton Agent Multi-Agent Syst 13, 429–461 (2006). https://doi.org/10.1007/s10458-006-0015-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10458-006-0015-x

Keywords

Search

Navigation