By Topic

Randomized sampling for large zero-sum games

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Shaunak D. Bopardikar ; Department of Electrical and Computer Engineering, University of California at Santa Barbara, USA ; Alessandro Borri ; João P. Hespanha ; Maria Prandini
more authors

This paper addresses the solution of large zero-sum matrix games using randomized methods. We provide a procedure by which a player can compute mixed policies that, with high probability, are security policies against an adversary that is also using randomized methods to solve the game. The computational savings result from solving subgames that are much smaller than the original game and we provide bounds on how large these subgames should be to guarantee the desired high probability. We propose two methodologies to solve this problem. The first provides a game-independent bound on the size of the subgames that can be computed a-priori. The second procedure is useful when computation limitations prevent a player from satisfying the first a-priori bound and provides a high-probability a-posteriori bound on how much the outcome of the game can violate the precomputed security level. All our probabilistic bounds are independent of the size of the original game and could, in fact, apply to games with continuous action spaces. To demonstrate the usefulness of these results, we apply them to solve a hide-and-seek game that exhibits exponential complexity.

Published in:

49th IEEE Conference on Decision and Control (CDC)

Date of Conference:

15-17 Dec. 2010