Learning to win process-control games watching game-masters

Case, John, Ott, Matthias, Sharma, Arun, & Stephan, Frank (2002) Learning to win process-control games watching game-masters. Information and Computation, 174(1), pp. 1-19.

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The present paper focuses on some interesting classes of process-control games, where winning essentially means successfully controlling the process. A master for one of these games is an agent who plays a winning strategy. In this paper we investigate situations in which even a complete model (given by a program) of a particular game does not provide enough information to synthesize—even incrementally—a winning strategy. However, if in addition to getting a program, a machine may also watch masters play winning strategies, then the machine is able to incrementally learn a winning strategy for the given game. Studied are successful learning from arbitrary masters and from pedagogically useful selected masters. It is shown that selected masters are strictly more helpful for learning than are arbitrary masters. Both for learning from arbitrary masters and for learning from selected masters, though, there are cases where one can learn programs for winning strategies from masters but not if one is required to learn a program for the master's strategy itself. Both for learning from arbitrary masters and for learning from selected masters, one can learn strictly more by watching m+1 masters than one can learn by watching only m. Last, a simulation result is presented where the presence of a selected master reduces the complexity from infinitely many semantic mind changes to finitely many syntactic ones.

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3 citations in Scopus
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3 citations in Web of Science®

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ID Code: 37260
Item Type: Journal Article
Refereed: Yes
DOI: 10.1006/inco.2000.2946
ISSN: 0890-5401
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTATION THEORY AND MATHEMATICS (080200)
Divisions: Current > QUT Faculties and Divisions > Division of Research and Commercialisation
Deposited On: 27 Sep 2010 02:13
Last Modified: 10 Aug 2011 17:37

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