Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Learning by analogical reasoning in general problem-solving
Learning by analogical reasoning in general problem-solving
Case-based reasoning
Planning from second principles
Artificial Intelligence
Building and refining abstract planning cases by change of representation language
Journal of Artificial Intelligence Research
Remembering to add: competence-preserving case-addition policies for case-base maintenance
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
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
Releasing Memory Space through a Case-Deletion Policy with a Lower Bound for Residual Competence
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
An Accurate Adaptation-Guided Similarity Metric for Case-Based Planning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
| Hi-index | 0.00 |
The majority of case-based planning systems consider an infinite case memory to store their cases. However, the size of the case memory is limited and it can become a barrier for case-based systems efficiency when it is full. This paper presents a method that refines and abstracts cases in order to release memory space for a new case. However, in some situations, some cases must be chosen to be deleted, and the method incorporates a case-deletion policy that achieves a lower bound for coverage depletion. Besides this paper can deal with a limited quantity of memory to store cases, the case-deletion policy also reaches better results for coverage-preserving than the case-addition policy proposed by Zhu and Yang [11].