A new approach to compositional adaptation based on optimizing the global distance function and its application in an intelligent tutoring system

In this paper, we propose a new approach to compositional adaptation based on the idea of constituting the final solution in a way that its global difference with a set of solutions belonging to the retrieved cases can get minimized. Within this respect, the normalized distance between the current problem and each retrieved case is taken into account, using a global distance function, which makes use of the normalized local distances between the candidate final solution and the retrieved cases' solutions as the variables, and some coefficients as its parameters. Here, an approach based on secondary CBR can be used to determine the optimal values of these coefficients based on their past experiences in characterization of the global distance function. An example is illustrated in the paper, which shows the utility of this approach fro rearranging the necessary coursewares for students in the realm of intelligent tutoring systems.

[1]  Claude Frasson,et al.  SARA: A Case-Based Student Modelling System , 1998, EWCBR.

[2]  Ralph Bergmann,et al.  Techniques and Knowledge Used for Adaptation During Case-Based Problem Solving , 1998, IEA/AIE.

[3]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[4]  Syed Sibte Raza Abidi.  A case base reasoning framework to author personalized health maintenance information , 2002, Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002).

[5]  Hans-Jörg Schek,et al.  A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces , 1998, VLDB.

[6]  K. Badie,et al.  Learning through estimating optimal formats for problem solving modules , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[7]  Claude Frasson,et al.  Student Modelling by Case Based Reasoning , 1998, Intelligent Tutoring Systems.

[8]  Charu C. Aggarwal,et al.  On the Surprising Behavior of Distance Metrics in High Dimensional Spaces , 2001, ICDT.

[9]  Syed Sibte Raza Abidi.  Designing Adaptive Hypermedia for Internet Portals: A Personalization Strategy Featuring Case Base Reasoning with Compositional Adaptation , 2002, IBERAMIA.

[10]  Panos M. Pardalos,et al.  Introduction to Global Optimization , 2000, Introduction to Global Optimization.

[11]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[12]  David G. Stork,et al.  Pattern Classification , 1973 .

[13]  Kambiz Badie,et al.  A Compositional Approach to Solution Adaptation in Case-Based Reasoning and its Application to Tutoring Library , 2000 .

[14]  J. Gower,et al.  Metric and Euclidean properties of dissimilarity coefficients , 1986 .

[15]  Manuela M. Veloso,et al.  Prodigy/Analogy: Analogical Reasoning in General Problem Solving , 1993, EWCBR.

[16]  Nikolaos M. Avouris,et al.  Case-based reasoning in environment monitoring applications , 1994, Appl. Artif. Intell..

[17]  Stefan Wess,et al.  Case-Based Reasoning Technology: From Foundations to Applications , 1998, Lecture Notes in Computer Science.

[18]  Teresa Larkin-Hein,et al.  Research on learning style: applications in the physics and engineering classrooms , 2001, IEEE Trans. Educ..

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .