Opportunistic Acquisition of Adaptation Knowledge and Cases — The IakA Approach

Abstract

Abstract. A case-based reasoning system relies on different knowledge containers, including cases and adaptation knowledge. The knowledge acquisition that aims at enriching these containers for the purpose of improving the accuracy of the CBR inference may take place during design, maintenance, and also on-line, during the use of the system. This paper describes IakA, an approach to on-line acquisition of cases and adaptation knowledge based on interactions with an oracle (a kind of "ideal expert"). IakA exploits failures of the CBR inference: when such a failure occurs, the system interacts with the oracle to repair the knowledge base. IakA-NF is a prototype for testing IakA in the domain of numerical functions with an automatic oracle. Two experiments show how IakA opportunistic knowledge acquisition improves the accuracy of the CBR system inferences. The paper also discusses the possible links between IakA and other knowledge acquisition approaches.

A complete chronicle discovery approach: application to activity analysis

“…Secondly, the trajectory of HCDA in the chronicle exploration space can be highly customized through the implementation of take_first_from. We have not emphasized this aspect in this article because the only heuristic functions we have implemented and tested so far are very basic: LIFO, FIFO, random, and ordered, but we plan to implement a more “intelligent” heuristic function that learns knowledge opportunely from the analyst (Cordier ) and that uses existing interestingness measures (Geng & Hamilton ) in order to direct the exploration of chronicles towards those that are potentially the most interesting for the analyst first.…”

Case-Based Reasoning on E-Community Knowledge

“…Usually, user feedback on proposed answers in CBR systems allows to judge the quality of the new cases and to repair a failed adaptation [19]. However, some authors think that the feedback approach is insufficient (by observing missing or delayed feedback) and that meta-knowledge is more relevant to improve the reasoning results [20].…”

Opportunistic Adaptation Knowledge Discovery

“…The first one was implemented in the CabamakA system [5] and learns AK from differences between cases by the means of knowledge discovery techniques (section 4.1). The second one was implemented in the IakA system [8] and acquires adaptation knowledge at problem-solving time through interactions with the user (section 4.2).…”