Competence Guided Casebase Maintenance for Compositional Adaptation Applications

Mathew, Ditty; Chakraborti, Sutanu

doi:10.1007/978-3-319-47096-2_18

Cited by 3 publications

(1 citation statement)

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“…This paper is a an abridged report of the paper entitled "Competence Guided Casebase Maintenance for Compositional Adaptation Applications" that appears in the Proceedings of International Conference on Case-Based Reasoning, [Mathew and Chakraborti, 2016].…”

Section: Acknowledgmentsmentioning

confidence: 99%

Competence Guided Model for Casebase Maintenance

Mathew

Chakraborti

2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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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, we address the compositional adaptation process (of which single case adaptation is a special case) during casebase maintenance by proposing a case competence model for which we propose a measure called retention score to estimate the retention quality of a case. We also propose a revised algorithm based on the retention score to estimate the competent subset of a casebase. We used synthetic datasets to test the effectiveness of the competent subset obtained from the proposed model. We also applied this model 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.

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Section: Acknowledgmentsmentioning

confidence: 99%