sam.ganzfried@gmail.com
Sam Ganzfried
Publications (by topic) {Google Scholar}
Sam Ganzfried. 2023. Empirical Analysis of Fictitious Play for Nash Equilibrium Computation in Multiplayer Games. Subsumes 2020 article "Fictitious Play Outperforms Counterfactual Regret Minimization" (video presentation) and 2022 article "Random Initialization Solves Shapley's Fictitious Play Counterexample."
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. 2023. Observable Perfect Equilibrium. In Proceedings of the Conference on Decision and Game Theory for Security (GameSec). Video presentation.
Sam Ganzfried. 2022. Stable Relationships.
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. Human Strategic Decision Making in Parametrized Games. Mathematics, 10(7), 1147. Special Issue "Game Theory and Artificial Intelligence."
Sam Ganzfried. 2023. Safe Equilibrium. In Proceedings of the IEEE Conference on Decision and Control (CDC). Video presentation.
Sam Ganzfried. 2022. Fictitious Play with Maximin Initialization. In Proceedings of the IEEE Conference on Decision and Control (CDC). Video presentation.
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." First algorithm for solving continuous Blotto game. The Blotto game is a well-studied model of resource allocation with applications to national security, voting, and auctions. Initial work constructed analytical solutions of continuous models, and recently efficient algorithms have been developed for a discrete model. Algorithm applies to both perfect and imperfect information, and applies to continuous games in general beyond Blotto games. Experiments on a continuous Blotto game with asymmetric imperfect information.
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). First algorithm for computing Nash equilibrium in multiplayer stochastic games with imperfect information that extends throughout game play, applied to a realistic national security scenario. Best paper award.
Sam Ganzfried. 2020. Fast Complete Algorithm for Multiplayer Nash Equilibrium. New algorithm for computing Nash equilibrium in normal-form games with three or more players that outperforms all prior algorithms. Video presentation.
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). Video presentation.
Max Chiswick and Sam Ganzfried. 2020. Prediction of Bayesian Intervals for Tropical Storms. In Proceedings of the Florida Artificial Intelligence Research Society Conference (FLAIRS). Short version and oral presentation in Proceedings of the 2020 ICLR Workshop Tackling Climate Change with Machine Learning.
Sam Ganzfried and Max Chiswick. 2020. Most Important Fundamental Rule of Poker Strategy. In Proceedings of the Florida Artificial Intelligence Research Society Conference (FLAIRS). New human-understandable rule that outperforms the popular "Minimum Defense Frequency" (MDF) rule by over 60%. MDF is used to justify a high-stakes poker decision in this recent video clip (11:55).
Sam Ganzfried. Mistakes in Games. 2019. In Proceedings of the International Conference on Distributed Artificial Intelligence (DAI). Video presentation.
Sam Ganzfried, Austin Nowak, and Joannier Pinales. 2018. Successful Nash Equilibrium Agent for a Three-Player Imperfect-Information Game. Games, 9(2), 33. Feature article.
Sheila Alemany, Jonathan Beltran, Adrian Perez, and Sam Ganzfried. 2019.
Predicting Hurricane Trajectories using a Recurrent Neural Network. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
Sam Ganzfried and Farzana Yusuf. 2018. Optimal Weighting for Exam Composition. Education Sciences 8(1), 36. Special issue "Artificial Intelligence and Education."
Sam Ganzfried and Farzana Yusuf. 2017. Computing Human-Understandable Strategies: Deducing Fundamental Rules of Poker Strategy. Games, 8(4), 49. Invited feature article.
New approach for computing strong game-theoretic strategies that can be easily understood by humans. This approach has enabled us to conclude several new fundamental rules of poker strategy, for example when to make a very small bet and when to make an extremely large one. Unconventional bet sizes were critical for recent poker agents such as Claudico (which I created) and Libratus.
Sam Ganzfried and Farzana Yusuf. 2019. Optimal Number of Choices in Rating Contexts. Big Data and Cognitive Computing, 3(3), 48. Special Issue "Computational Models of Cognition and Learning." Theory, simulations, and experimental results for the optimal number of choices to use from a small discrete set (which is used to approximate a large underlying choice set), with applications including online dating, paper reviewing, and exam grading. Counterintuitively, allowing more options is not always best, and fewer options is optimal surprisingly often.
Sam Ganzfried and Qingyun Sun. 2018. Bayesian Opponent Exploitation in Imperfect-Information Games. In Proceedings of the Conference on Computational Intelligence and Games (CIG). See also the extended version containing proofs and additional analysis. First exact algorithm for opponent exploitation in Bayesian setting in imperfect-information games using natural prior models (Dirichlet and uniform).
Sam Ganzfried. 2017. Reflections on the First Man versus Machine No-Limit Texas Hold ‘em Competition. AI Magazine 38(2) summer issue. Early version appeared as feature article in 2015 SIGecom Exchanges, Volume 14.2. See also ArXiv version. Many of the weaknesses of Claudico and future directions for improvement are described in detail in this article, which were used subsequently to implement Libratus that defeated the strongest humans in two-player no-limit Texas hold 'em.
Sam Ganzfried. 2015. Computing Strong Game-Theoretic Strategies and Exploiting Suboptimal Opponents in Large Games. PhD dissertation, Computer Science Department, Carnegie Mellon University. Available as CMU technical report CMU-CS-15-104. Thesis proposal.
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). Early version appeared at Workshop on Computer Poker and Imperfect Information at the AAAI Conference on Artificial Intelligence, 2013. See also Endgame Solving: The Surprising Breakthrough that Enabled Superhuman Two-Player No-Limit Texas Hold 'em Play from 2017 International Conference on Game Theory.
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). Short version appeared in Proceedings of the Demonstrations Program at the AAAI Conference on Artificial Intelligence, 2015. *Listed alphabetically. Describes architecture of two-player no-limit Texas hold 'em agent Tartanian7, which won the 2014 AAAI Annual Computer Poker Competition.
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). Algorithm for game abstraction that takes into account trajectories of private information revelation across all rounds.
Sam Ganzfried and Tuomas Sandholm. 2013. Action Translation in Extensive-Form Games with Large Action Spaces: Axioms, Paradoxes, and the Pseudo-Harmonic Mapping. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). Algorithm for interpreting opponents' actions that have been removed from game abstraction, which is used by strongest no-limit Texas hold 'em agents.
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). Algorithms and theory for when it is possible to deviate from just repeatedly playing one-shot Nash equilibrium to exploit opponents' weaknesses in two-player zero-sum games.
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). Theory and experiments for techniques for post-processing approximate equilibrium strategies of game abstractions.
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). Algorithm integrates approximate equilibrium prior with learning from observations and does not require historical data or domain-specific features. First approach for integrating behavioral strategy constraints in context of opponent exploitation.
Sam Ganzfried. 2011. Computing Strong Game-Theoretic Strategies in Jotto. In Proceedings of the Conference on Advances in Computer Games (ACG). Jotto is a word game similar to Wordle. See more recent research on Jotto and Wordle by Peter Norvig.
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). Extended version as CMU technical report CMU-CS-10-105. Algorithm exploits human-understandable qualitative strategy model to improve equilibrium finding in Bayesian games with application to limit Texas hold 'em.
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). New algorithm with theoretical results for computing approximate Nash equilibrium in multiplayer stochastic imperfect-information games with application to realistic three-player poker tournament. See 2020 DAI conference article above for successful application of extension of the algorithm to a national security scenario for naval strategic planning.
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). Algorithm computes approximate Nash equilibrium in multiplayer stochastic imperfect-information games, with application to realistic three-player poker tournament.