Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '98 Proceedings of the Fifteenth International 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
Formalizing a Language for Institutions and Norms
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
Case Exchange Strategies in Multiagent Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Boosting CBR Agents with Genetic Algorithms
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Case-based learning from proactive communication
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Cooperative multiagent learning
Adaptive agents and multi-agent systems
Multi-agent learning by distributed feature extraction
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
Self-organising hierarchical retrieval in a case-agent system
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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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 that address the issue of agents having a biased view of the data. The outcome of bartering is an improvement of individual agent performance and of overall multiagent system performance that equals the ideal situation where all agents have an unbiased view of the data. We also present empirical results illustrating the robustness of the case bartering process for several configurations of the multiagent system and for three different CBR techniques.