Machine Learning
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification on Data with Biased Class Distribution
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Lazy Induction of Descriptions for Relational Case-Based Learning
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Learning When to Collaborate among Learning Agents
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Modelling the Competence of Case-Bases
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Bidding Mechanisms for Data Allocation in Multi-Agent Environments
ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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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.