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.
CITATION STYLE
Santos, P., Colton, S., & Magee, D. (2006). Predictive and descriptive approaches to learning game rules from vision data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4140 LNAI, pp. 349–359). Springer Verlag. https://doi.org/10.1007/11874850_39
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