Papers by topic

Sam Ganzfried

National Security

Sam Ganzfried. 2022. Best Response Computation in Multiplayer Imperfect-Information Stochastic Games. In Proceedings of the Florida Artificial Intelligence Research Society Conference (FLAIRS).

Sam Ganzfried. 2021. Algorithm for Computing Approximate Nash Equilibrium in Continuous Games with Application to Continuous Blotto. Games, 12(2), 47. Special issue "Economics of Conflict and Terrorism."

Sam Ganzfried. 2021. Computing Nash Equilibria in Multiplayer DAG-Structured Stochastic Games with Persistent Imperfect Information. In Proceedings of the Conference on Decision and Game Theory for Security (GameSec).

Sam Ganzfried, Conner Laughlin, and Charles Morefield. 2020. Parallel Algorithm for Approximating Nash Equilibrium in Multiplayer Stochastic Games with Application to Naval Strategic Planning. In Proceedings of the International Conference on Distributed Artificial Intelligence (DAI).

Stochastic Games

Sam Ganzfried. 2022. Best Response Computation in Multiplayer Imperfect-Information Stochastic Games. In Proceedings of the Florida Artificial Intelligence Research Society Conference (FLAIRS).

Sam Ganzfried and Tuomas Sandholm. 2008. Computing an Approximate Jam/Fold Equilibrium for 3-Player No-Limit Texas Hold'em Tournaments. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Continuous Games

Sam Ganzfried and Tuomas Sandholm. 2010. Computing Equilibria by Incorporating Qualitative Models. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Opponent Modeling

Sam Ganzfried. 2023. Nonparametric Strategy Test.

Sam Ganzfried, Kevin A. Wang, and Max Chiswick. 2022. Bayesian Opponent Modeling in Multiplayer Imperfect-Information Games.

Sam Ganzfried. Mistakes in Games. 2019. In Proceedings of the International Conference on Distributed Artificial Intelligence (DAI).

Sam Ganzfried and Qingyun Sun. 2018. Bayesian Opponent Exploitation in Imperfect-Information Games. In Proceedings of the Conference on Computational Intelligence and Games (CIG).

Sam Ganzfried and Tuomas Sandholm. 2015. Safe Opponent Exploitation. ACM Transactions on Economics and Computation (TEAC), 3(2), 8:1-28. Early version in Proceedings of the 2012 ACM Conference on Electronic Commerce (EC).

Sam Ganzfried and Tuomas Sandholm. 2011. Game Theory-Based Opponent Modeling in Large Imperfect-Information Games. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Human-Understandable Strategies

Sam Ganzfried. 2022. Human Strategic Decision Making in Parametrized Games. Mathematics, 10(7), 1147. Special Issue "Game Theory and Artificial Intelligence."

Sam Ganzfried and Max Chiswick. 2020. Most Important Fundamental Rule of Poker Strategy. In Proceedings of the Florida Artificial Intelligence Research Society Conference (FLAIRS).

Sam Ganzfried and Farzana Yusuf. 2017. Computing Human-Understandable Strategies: Deducing Fundamental Rules of Poker Strategy. Invited feature article at Games, 8(4), 49.

Sam Ganzfried and Tuomas Sandholm. 2010. Computing Equilibria by Incorporating Qualitative Models. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Large Games

Sam Ganzfried and Tuomas Sandholm. 2015. Endgame Solving in Large Imperfect-Information Games. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Noam Brown*, Sam Ganzfried*, and Tuomas Sandholm. 2015. Hierarchical Abstraction, Distributed Equilibrium Computation, and Post-Processing, with Application to a Champion No-Limit Texas Hold'em Agent. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS). *Listed alphabetically.

Sam Ganzfried and Tuomas Sandholm. 2014. Potential-Aware Imperfect-Recall Abstraction with Earth Mover's Distance in Imperfect-Information Games. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).

Sam Ganzfried, Tuomas Sandholm, and Kevin Waugh. 2012. Strategy Purification and Thresholding: Effective Non-Equilibrium Approaches for Playing Large Games. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Solution Concepts

Sam Ganzfried. 2023. Observable Perfect Equilibrium. In Proceedings of the Conference on Decision and Game Theory for Security (GameSec).

Sam Ganzfried. 2023. Safe Equilibrium. In Proceedings of the IEEE Conference on Decision and Control (CDC).

Multiplayer Games

Sam Ganzfried. 2023. Empirical Analysis of Fictitious Play for Nash Equilibrium Computation in Multiplayer Games.

Sam Ganzfried, Kevin A. Wang, and Max Chiswick. 2022. Bayesian Opponent Modeling in Multiplayer Imperfect-Information Games.

Sam Ganzfried. 2022. Best Response Computation in Multiplayer Imperfect-Information Stochastic Games. In Proceedings of the Florida Artificial Intelligence Research Society Conference (FLAIRS).

Sam Ganzfried. 2022. Fictitious Play with Maximin Initialization. In Proceedings of the IEEE Conference on Decision and Control (CDC).

Sam Ganzfried. 2020. Fast Complete Algorithm for Multiplayer Nash Equilibrium.

Sam Ganzfried, Conner Laughlin, and Charles Morefield. 2020. Parallel Algorithm for Approximating Nash Equilibrium in Multiplayer Stochastic Games with Application to Naval Strategic Planning. In Proceedings of the International Conference on Distributed Artificial Intelligence (DAI).

Sam Ganzfried, Austin Nowak, and Joannier Pinales. 2018. Successful Nash Equilibrium Agent for a Three-Player Imperfect-Information Game. Feature article at Games, 9(2), 33.

Sam Ganzfried and Tuomas Sandholm. 2009. Computing Equilibria in Multiplayer Stochastic Games of Imperfect Information. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).

Sam Ganzfried and Tuomas Sandholm. 2008. Computing an Approximate Jam/Fold Equilibrium for 3-Player No-Limit Texas Hold'em Tournaments. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).