Competence guided model for casebase maintenance
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 footprintbased competence model in compositional adaptation applications.
A generalized case competence model for casebase maintenance
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.
Towards creating pedagogic views from encyclopedic resources
This paper identifies computational challenges in restructuring encyclopedic resources (like Wikipedia or thesauri) to reorder concepts with the goal of helping learners navigate through a concept network without getting trapped in circular dependencies between concepts. We present approaches that can help content authors identify regions in the concept network, that after editing, would have maximal impact in terms of enhancing the utility of the resource to learners.
Towards compiling textbooks from Wikipedia
In this paper, we explore challenges in compiling a pedagogic resource like a textbook on a given topic from relevant Wikipedia articles, and present an approach towards assisting humans in this task. We present an algorithm that attempts to suggest the textbook structure from Wikipedia based on a set of seed concepts (chapters) provided by the user. We also conceptualize a decision support system where users can interact with the proposed structure and the corresponding Wikipedia content to improve its pedagogic value. The proposed algorithm is implemented and evaluated against the outline of online textbooks on five different subjects. We also propose a measure to quantify the pedagogic value of the suggested textbook structure.
An optimal footprint method for case-base maintenance
In Case-Based Reasoning (CBR), new problems are solved by retrieving similar previously solved cases and adapting their solutions. The new case is then stored appropriately in the case-base for future use. It is a fundamental problem to control the growth of case-base and the case-base maintenance step retains cases in the case-base based on an estimate of their usefulness in solving new problems. We propose an optimization formulation to identify an optimal set of representative cases called the optimal footprint of the case-base. The optimization formulation ensures that the optimal footprint set strikes a right trade-off between minimizing the number of cases and maximizing their ability to solve the remaining cases in the case-base. This trade-off is studied empirically in this paper. We also illustrate the trade-off between the size and performance of optimal footprint in the context of regression.
An Optimal Case-Base Maintenance Method for Compositional Adaptation Applications
Case-base maintenance method aims at maintaining a compressed case-base which is useful for solving future problems effectively. In this paper, we propose an optimization formulation to arrive at a compressed case-base that can find a solution for the rest of the cases in the case-base that involves compositional adaptation process. The objective of the optimization problem is to minimize the footprint set size and maximize the quality of solutions that can be adapted from the footprint set. We empirically studied the proposed formulation on four different datasets and the results show that the proposed model is effective and overcomes the limitation of the existing optimal footprint method in compositional adaptation applications.
Competence guided casebase maintenance for compositional adaptation applications
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 the global sense. 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 the casebase. We used regression 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.