Noam Brown | AI for Imperfect-Information Games: Poker and Beyond

Monday 10 May 2021

Abstract: The field of artificial intelligence has had a number of high-profile successes in the domain of perfect-information games like chess or Go where all participants know the exact state of the world. But real-world strategic interactions typically involve hidden information, such as in negotiations, cybersecurity, and financial markets. Past AI techniques fall apart in these settings, with poker serving as the classic benchmark and challenge problem.

In this talk, I will cover the key breakthroughs behind Libratus and Pluribus, the first AI agents to defeat elite human professionals in two-player no-limit poker and multiplayer no-limit poker, respectively. In particular, I will discuss new forms of the counterfactual regret minimization equilibrium-finding algorithm and breakthroughs that enabled depth-limited search for imperfect-information games to be conducted orders of magnitude faster than previous algorithms. Finally, I will conclude with a discussion on recent work combining the previously separate threads of research on perfect-information and imperfect-information games.

Papers:

Bio: Noam Brown is a Research Scientist at Facebook AI Research working on multi-agent artificial intelligence, with a particular focus on imperfect-information games. He co-created Libratus and Pluribus, the first AIs to defeat top humans in two-player no-limit poker and multiplayer no-limit poker, respectively. He has received the Marvin Minsky Medal for Outstanding Achievements in AI, was named one of MIT Tech Review's 35 Innovators Under 35, and his work on Pluribus was named by Science Magazine to be one of the top 10 scientific breakthroughs of the year. Noam received his PhD from Carnegie Mellon University, where he received the School of Computer Science Distinguished Dissertation Award.

Moderated by: Michal Šustr

[NLAI]

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