A bartering approach to improve multiagent learning
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
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
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
When Two Case Bases Are Better than One: Exploiting Multiple Case Bases
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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 retain where the agents take in consideration that they are not learning in isolation but in a multiagent system. We also present case bartering as an effective strategy when the agents have a biased view of the data. The outcome of both case retain and bartering is an improvement of individual agent performance and overall multiagent system performance. We also present empirical results comparing all the strategies proposed.