Abstract
Most research in computer chess has focused on creating an excellent chess player, with relatively little concern given to modeling how humans play chess. The research reported in this article is aimed at investigating knowledge-based chess in the context of building a prototype chess tutor, UMRAO, which helps students learn how to play bishop-pawn endgames. In tutoring it is essential to take a knowledge-based approach, since students must learn how to manipulate strategic concepts, not how to carry out large-scale lookahead searches.
UMRAO uses an extension of Michie's advice language to represent expert and novice chess plans. For any given endgame, the system is able to compile the plans into a strategy graph, which elaborates strategies (both well formed and ill formed) that students might use as they solve the endgame problem. A strategy graph can be compiled “off-line,” where real-time performance is not important. Later, during tutoring, the strategy graph can be accessed quickly in order to understand a student's moves in terms of his or her strategies. With such understanding, UMRAO is able to provide appropriate knowledge-based feedback to the student. Anderson et al. have called this tutoring paradigm “model tracing,” but in the chess domain model tracing can be used without the need for immediate feedback that Anderson has required in his more complex abstract problem-solving domains. The chess domain thus allows experimentation with a variety of tutoring styles that range from immediate feedback to optional feedback, from strict tutor control of the feedback to student initiative in the choice of feedback. This points out UMRAO's most promising contribution: re-establishing chess as a vehicle for research in other areas of artificial intelligence, in this case intelligent tutoring systems. UMRAO also makes technical contributions to knowledge-based chess and to intelligent tutoring as well.
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Gadwal, D., Greer, J.E. & McCalla, G.I. Tutoring bishop-pawn endgames: An experiment in using knowledge-based chess as a domain for intelligent tutoring. Appl Intell 3, 207–224 (1993). https://doi.org/10.1007/BF00871938
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DOI: https://doi.org/10.1007/BF00871938