Instance-Based Learning Algorithms
Machine Learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Utility Problem Analysed: A Case-Based Reasoning Perspective
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Remembering to Add: Competence-preserving Case-Addition Policies for Case Base Maintenance
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning to form dynamic committees
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
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
Adaptive case-based reasoning using retention and forgetting strategies
Knowledge-Based Systems
A case-based approach for indoor location
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Multi-agent case-based reasoning for cooperative reinforcement learners
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
EGAL: exploration guided active learning for TCBR
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
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Empirical experiments have shown that storing every case does not automatically improve the accuracy of a CBR system. Therefore, several retain policies have been proposed in order to select which cases to retain. However, all the research done in case retention strategies is done in centralized CBR systems. We focus on multiagent CBR systems, where each agent has a local case base, and where each agent can interact with other agents in the system to solve problems in a collaborative way. We propose several case retention strategies that directly deal with the issue of being in a multiagent CBR system. Those case retention strategies combine ideas from the CBR case retain strategies and from the active learning techniques. Empirical results show that strategies that use collaboration with other agents outperform those strategies where the agents work in isolation. We present experiments in two different scenarios, the first one allowing multiple copies of one case and the second one only allowing one copy of each case. Although it may seem counterintuitive, we show and explain why not allowing multiple copies of each case achieves better results.