Predictive and descriptive approaches to learning game rules from vision data

Paulo SantosCOLTON, S.MAGEE, D.2022-01-122022-01-122006-10-23SANTOS, 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, 2006.1611-3349https://repositorio.fei.edu.br/handle/FEI/4357Systems 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.Acesso RestritoPredictive and descriptive approaches to learning game rules from vision dataArtigo de evento10.1007/11874850_39