dc.contributor.author | Paulo Santos | |
dc.contributor.author | COLTON, S. | |
dc.contributor.author | MAGEE, D. | |
dc.date.accessioned | 2022-01-12T22:05:42Z | |
dc.date.accessioned | 2024-02-27T16:29:58Z | |
dc.date.available | 2022-01-12T22:05:42Z | |
dc.date.available | 2024-02-27T16:29:58Z | |
dc.date.issued | 2006-10-23 | |
dc.identifier.citation | 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. | |
dc.identifier.issn | 1611-3349 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12032/122163 | |
dc.description.abstract | 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. | |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.rights | Acesso Restrito | |
dc.title | Predictive and descriptive approaches to learning game rules from vision data | |
dc.type | Artigo de evento | |