Representations and solutions for game-theoretic problems
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Computing an approximate jam/fold equilibrium for 3-player no-limit Texas Hold'em tournaments
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A new algorithm for generating equilibria in massive zero-sum games
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Probabilistic state translation in extensive games with large action sets
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Using counterfactual regret minimization to create competitive multiplayer poker agents
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Computing equilibria by incorporating qualitative models?
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Artificial Intelligence
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Lossy stochastic game abstraction with bounds
Proceedings of the 13th ACM Conference on Electronic Commerce
Accelerating best response calculation in large extensive games
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Strategy purification and thresholding: effective non-equilibrium approaches for playing large games
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Case-based strategies in computer poker
AI Communications
Online implicit agent modelling
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Evaluating state-space abstractions in extensive-form games
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Extensive games can be used to model many scenarios in which multiple agents interact with an environment. There has been considerable recent research on finding strong strategies in very large, zero-sum extensive games. The standard approach in such work is to employ abstraction techniques to derive a more tractably sized game. An extensive game solver is then employed to compute an equilibrium in that abstract game, and the resulting strategy is presumed to be strong in the full game. Progress in this line of research has focused on solving larger abstract games, which more closely resemble the full game. However, there is an underlying assumption that by abstracting less, and solving a larger game, an agent will have a stronger strategy in the full game. In this work we show that this assumption is not true in general. Refining an abstraction can actually lead to a weaker strategy. We show examples of these abstraction pathologies in a small game of poker that can be analyzed exactly. These examples show that pathologies arise when abstracting both chance nodes as well as a player's actions. In summary, this paper shows that the standard approach to finding strong strategies for large extensive games rests on shaky ground.