(Enter summary)
Abstract: this paper, games are played by two players. One will be
called the adversary and the other the strategy learning algorithm.
We think of the adversary as a fixed resource-bounded
computational mechanism that may be playing a strategy quite
different from the minimax optimal strategy for the game. The
strategy learning algorithm will be a polynomial-time algorithm
attempting to learn, from repeated plays, a profitable
strategy for playing against the adversary.
As is typical, a game in our... (Update)
Context of citations to this paper: More
.... design ( 4] provides an overview) or data driven adaptation of learning algorithms (especially in work on game learning, cf. e.g. [5]) is employed to yield the desired behaviour. In our work, we followed a different approach. We investigated the possibilities of...
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BibTeX entry: (Update)
Y. Freund, M. Kearns, Y. Mansour, D. Ron, R. Rubinfeld, and R. Schapire. Efficient algorithms for learning to play repeated games against computationally bounded adversaries. In Proceedings of the Thirty Seventh Annual Symposium on Foundations of Computer Science, pages 332--341, 1996. http://citeseer.ist.psu.edu/article/freund95efficient.html More
@inproceedings{ freund95efficient,
author = "Yoav Freund and Michael J. Kearns and Yishay Mansour and Dana Ron and Ronitt Rubinfeld and Robert E. Schapire",
title = "Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries",
booktitle = "{IEEE} Symposium on Foundations of Computer Science",
pages = "332-341",
year = "1995",
url = "citeseer.ist.psu.edu/article/freund95efficient.html" }
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