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Prague, Czech Republic

Tozicka J.,Agent Technology Center | Balata J.,Czech Technical University | Mikovec Z.,Czech Technical University
12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 | Year: 2013

Current UAV control systems can plan optimal trajectories with respect to predefined constraints (waypoints, no-fly zones). However when the operator is not satisfied with planned trajectories it is usually very complicated to force the system to change the trajectories in desired way. In this demonstration we solve this problem by innovative UAV control display based on diverse planning algorithm. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. Source


Bosansky B.,Agent Technology Center
12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 | Year: 2013

We investigate iterative algorithms for computing exact Nash equilibria in two-player zero-sum extensive-form games. The algorithms use an algorithmic framework of double-oracle methods. The main idea is to restrict the game by allowing the players to play only some of the strategies, and then iteratively solve this restricted game and exploit fast best-response algorithms to add additional strategies to the restricted game for the next iteration. The experimental evaluation on different games shows that the double-oracle methods often provide significant improvement in running-time, and can find exact solution of much larger games compared to the existing approaches. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. Source


Bosansky B.,Agent Technology Center | Kiekintveld C.,University of Texas at El Paso | Lisy V.,Agent Technology Center | Pechoucek M.,Agent Technology Center
Frontiers in Artificial Intelligence and Applications | Year: 2012

We develop and evaluate a new exact algorithm for finding Nash equilibria of two-player zero-sum extensive-form games with imperfect information. Our approach is based on the sequence-form representation of the game, and uses an algorithmic framework of double-oracle methods that have been used successfully in other classes of games. The algorithm uses an iterative decomposition, solving restricted games and exploiting fast best-response algorithms to add additional sequences to the game over time. We demonstrate our algorithm on a class of adversarial graph search games motivated by real world border patrolling scenarios. The results indicate that our framework is a promising way to scale up solutions for extensive-form games, reducing both memory and computation time requirements. © 2012 The Author(s). Source


Bosansky B.,Agent Technology Center | Kiekintveld C.,University of Texas at El Paso | Lisy V.,Agent Technology Center | Pechoucek M.,Agent Technology Center
Journal of Artificial Intelligence Research | Year: 2014

Developing scalable solution algorithms is one of the central problems in computational game theory. We present an iterative algorithm for computing an exact Nash equilibrium for two-player zero-sum extensive-form games with imperfect information. Our approach combines two key elements: (1) the compact sequence-form representation of extensive-form games and (2) the algorithmic framework of double-oracle methods. The main idea of our algorithm is to restrict the game by allowing the players to play only selected sequences of available actions. After solving the restricted game, new sequences are added by finding best responses to the current solution using fast algorithms. We experimentally evaluate our algorithm on a set of games inspired by patrolling scenarios, board, and card games. The results show significant runtime improvements in games admitting an equilibrium with small support, and substantial improvement in memory use even on games with large support. The improvement in memory use is particularly important because it allows our algorithm to solve much larger game instances than existing linear programming methods. Our main contributions include (1) a generic sequence-form double-oracle algorithm for solving zero-sum extensive-form games; (2) fast methods for maintaining a valid restricted game model when adding new sequences; (3) a search algorithm and pruning methods for computing best-response sequences; (4) theoretical guarantees about the convergence of the algorithm to a Nash equilibrium; (5) experimental analysis of our algorithm on several games, including an approximate version of the algorithm. © 2014 AI Access Foundation. All rights reserved. Source

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