Assumptions and issues in text-based retrieval
Text-based intelligent systems
Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization
Machine Learning - Special issue on case-based reasoning
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Machine Learning
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Using introspective reasoning to refine indexing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Minds and Machines
Hi-index | 0.01 |
This paper discusses techniques that improve the performance of a case retrieval system, after it is deployed, as a result of the continued usage of the system, by remembering previous episodes of question answering. The user generates a request for information and the system responds with the retrieval of relevant case(s). A history of such transactional behavior over a given set of data is maintained by the system and used as a foundation for adapting its future retrieval behavior. With each transaction, the system acquires information about the usage of the system that is subsequently used to adjust the behavior of the system. This notion of a case retrieval system draws on a distinction between the system in isolation and the system as it is used for a particular set of cases. It also draws on distinctions between the designed system, the deployed system, and the system that emerges as it is used.