A generalized case competence model for casebase maintenance

Issue title: Case Based Reasoning

Guest editors: Belén Díaz Agudo and Ashok Goel

Article type: Research Article

Authors: Mathew, Ditty^{; *} | Chakraborti, Sutanu

Affiliations: Department of Computer Science and Engineering, Indian Institute of Technology, Madras, India. E-mails: ditty@cse.iitm.ac.in, sutanuc@cse.iitm.ac.in

Correspondence: [*] Corresponding author. E-mail: ditty@cse.iitm.ac.in.

Abstract: A competence guided casebase maintenance algorithm retains a case in the casebase if it is useful to solve many problems and ensures that the casebase is highly competent. In this paper, a generalized case competence model is proposed for casebase maintenance which addresses compositional adaptation of which single case adaptation is a special case. For this model, a measure called retention score is proposed to estimate the retention quality of a case. A revised algorithm is proposed to estimate the competent subset of the casebase using retention score. We also propose a weighted retention score measure which considers the problem solving ability of cases that are involved in arriving at a solution. The effectiveness of the competent subset obtained from the proposed model is tested using synthetic classification dataset and housing dataset. This model is also applied in a tutoring application and analyzed the competent subset of concepts in tutoring resources. Empirical results show that the proposed model is effective and overcomes the limitation of footprint based competence model in compositional adaptation applications.

Keywords: Casebase maintenance, case competence, footprint based competence model, compositional adaptation

DOI: 10.3233/AIC-170734

Journal: AI Communications, vol. 30, no. 3-4, pp. 295-309, 2017