CIMMEP: constrained integrated method for CBR maintenance based on evidential policies
Résumé
The quality of the proposed solutions by Case-Based Reasoning (CBR) systems is highly dependent on recorded experiences and their describing attributes. Hence, to keep them offering accurate and efficient responses for a long time frame, the maintenance of Case Bases (CB) and Vocabulary knowledge is required. However, maintenance operations are usually unable to exploit provided domain-experts knowledge although this kind of systems are widely applied in several real-life contexts. This offered prior knowledge is handled, in our work, in form of pairwise constraints: Regarding cases, Must-Link (ML) affirms that two given problems should have the same solution, and Cannot-Link (CL) informs that two problems cannot have the same solution. These constraints may also regard vocabulary knowledge in such a way that ML is generated when prior knowledge affirm that two given features offer correlated values, therefore, similar information, and CL is built when they provide different information. This paper proposes a new constrained & integrated method, named CIMMEP, encoding Constrained & Integrated Maintaining Method based on Evidential Policies, for maintaining both vocabulary and CB through eliminating redundancy and noisiness. Since CBR systems handle real-world experiences, which are full of uncertainty, CIMMEP manages this imperfection using a powerful tool called the belief function theory.
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