Forgetting Reinforced Cases

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.