Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Fish and Shrink. A Next Step Towards Efficient Case Retrieval in Large-Scale Case Bases
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Building Compact Competent Case-Bases
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Maintaining Unstructured Case Base
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
An Analysis of Case-Based Value Function Approximation by Approximating State Transition Graphs
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Transfer learning in real-time strategy games using hybrid CBR/RL
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
CBR for state value function approximation in reinforcement learning
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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To meet time constraints, a CBR system must control the time spent searching in the case base for a solution. In this paper, we presents the results of a case study comparing the proficiency of some criteria for forgetting cases, hence bounding the number of cases to be explored during retrieval. The criteria being considered are case usage, case value and case density. As we make use of a sequential game for our experiments, case values are obtained through training using reinforcement learning. Our results indicate that case usage is the most favorable criteria for selecting the cases to be forgotten prior to retrieval. We also have some indications that a mixture of case usage and case value can provide some improvements. However compaction of a case base using case density reveals less performing for our application.