Predictive and descriptive approaches to learning game rules from vision data
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7
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2006-10-23
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Paulo Santos
COLTON, S.
MAGEE, D.
COLTON, S.
MAGEE, D.
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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SANTOS, P.; COLTON, S.; MAGEE, D. Predictive and descriptive approaches to learning game rules from vision data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), p. 349-359, October, 2006.
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Systems able to learn from visual observations have a great deal of potential for autonomous robotics, scientific discovery, and many other fields as the necessity to generalise from visual observation (from a quotidian scene or from the results of a scientific enquiry) is inherent in various domains. We describe an application to learning rules of a dice game using data from a vision system observing the game being played. In this paper, we experimented with two broad approaches: (i) a predictive learning approach with the Progol system, where explicit concept learning problems are posed and solved, and (ii) a descriptive learning approach with the HR system, where a general theory is formed with no specific problem solving task in mind and rules are extracted from the theory. © Springer-Verlag Berlin Heidelberg 2006.