Abstract
Multiagent systems offer a new paradigm to organize AI Applications. We focus on the application of Case-Based Reasoning to Multiagent systems. CBR offers the individual agents the capability of autonomously learn from experience. In this paper we present a framework for collaboration among agents that use CBR. We present explicit strategies for case bartering in order improve individual case bases and reduce bias in the case bases. We also present empirical results illustrating the robustness of the case bartering process for several configurations of the multiagent system. Finally, a bias and variance analysis of the effects of bartering is included.
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References
E. Armengol and E. Plaza. Lazy induction of descriptions for relational case-based learning. In 12th European Conference on Machine Learning, 2001.
Steven J. Brams and Peter C. Fishburn. Approval Voting. Birkhauser, Boston, 1983.
Leo Breiman. Bagging predictors. Machine Learning, 24(2): 123–140, 1996.
Yoav Freund and Robert E. Schapire. Experiments with a new boosting algorithm. In Proc. 13th ICML, pages 148–146. Morgan Kaufmann, 1996.
Jerome H. Friedman. On bias, variance, 0/1-loss, and the curse-of-dimensionality. Data Mining and Knowledge Discovery, 1(1):55–77, 1997.
L. K. Hansen and P. Salamon. Neural networks ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(12):993–1001, 1990.
Ron Kohavi and David H. Wolpert. Bias plus variance decomposition for zero-one loss functions. In Lorenza Saitta, editor, Machine Learning: Proceedings of the Thirteenth International Conference, pages 275–283. Morgan Kaufmann, 1996.
S. Ontañón and E. Plaza. Learning when to collaborate among learning agents. In 12th European Conference on Machine Learning, 2001.
M. P. Perrone and L. N. Cooper. When networks disagree: Ensemble methods for hydrid neural networks. In Artificial Neural Networks for Speech and Vision. Chapman-Hall, 1993.
richard A. Olshen and L. Gordon. Almost sure consistent nonparametric regression from recursive partitioning schemes. Multivariate Analysis, 15:147–163, 1984.
R. Schwartz and S. Kraus. Bidding mechanisms for data allocation in multi-agent environments. In Agent Theories, Architectures, and Languages, pages 61–75, 1997.
B. Smyth and E. McKenna. Modelling the competence of case-bases. In EWCBR, pages 208–220, 1998.
Barry Smyth and Mark T. Keane. Remembering to forget: A competence-preserving case deletion policy for case-based reasoning systems. In IJCAI, pages 377–383, 1995.
S. Vucetic and Z. Obradovic. Classification on data with biased class distribution. In 12th European Conference on Machine Learning, 2001.
L. Vuurpijl and L. Schomaker. A framework for using multiple classifiers in a multiple-agent architecture. In Third International Workshop on Handwriting Analysis and Recognition, 1998.
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Ontañón, S., Plaza, E. (2002). Cooperative Case Bartering for Case-Based Reasoning Agents. In: Escrig, M.T., Toledo, F., Golobardes, E. (eds) Topics in Artificial Intelligence. CCIA 2002. Lecture Notes in Computer Science(), vol 2504. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36079-4_26
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DOI: https://doi.org/10.1007/3-540-36079-4_26
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