10 papers
Nash without Numbers: A Social Choice Approach to Mixed Equilibria in Context-Ordinal Games
Ian Gemp, Crystal Qian, Marc Lanctot +1
Nash equilibrium serves as a fundamental mathematical tool in economics and game theory. However, it classically assumes knowledge of player utilities, whereas economics generally…
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
Zun Li, Marc Lanctot, Kevin R. McKee +7
Opponent modeling methods typically involve two crucial steps: building a belief distribution over opponents' strategies, and exploiting this opponent model by playing a best respo…
An Efficient Algorithm for Thresholding Monte Carlo Tree Search
Shoma Nameki, Atsuyoshi Nakamura, Junpei Komiyama +1
We introduce the Thresholding Monte Carlo Tree Search problem, in which, given a tree and a threshold , a player must answer whether the root node value of $\math…
Evaluating Agents using Social Choice Theory
Marc Lanctot, Kate Larson, Yoram Bachrach +6
We argue that many general evaluation problems can be viewed through the lens of voting theory. Each task is interpreted as a separate voter, which requires only ordinal rankings o…
Soft Condorcet Optimization for Ranking of General Agents
Marc Lanctot, Kate Larson, Michael Kaisers +7
Driving progress of AI models and agents requires comparing their performance on standardized benchmarks; for general agents, individual performances must be aggregated across a po…
Learning in Games with Progressive Hiding
Benjamin Heymann, Marc Lanctot
When learning to play an imperfect information game, it is often easier to first start with the basic mechanics of the game rules. For example, one can play several example rounds…