5 research outputs found
Making the Best of Limited Memory in Multi-Player Discounted Sum Games
In this paper, we establish the existence of optimal bounded memory strategy
profiles in multi-player discounted sum games. We introduce a non-deterministic
approach to compute optimal strategy profiles with bounded memory. Our approach
can be used to obtain optimal rewards in a setting where a powerful player
selects the strategies of all players for Nash and leader equilibria, where in
leader equilibria the Nash condition is waived for the strategy of this
powerful player. The resulting strategy profiles are optimal for this player
among all strategy profiles that respect the given memory bound, and the
related decision problem is NP-complete. We also provide simple examples, which
show that having more memory will improve the optimal strategy profile, and
that sufficient memory to obtain optimal strategy profiles cannot be inferred
from the structure of the game.Comment: In Proceedings GandALF 2015, arXiv:1509.0685
Computing Optimal Strategies to Commit to in Stochastic Games
Significant progress has been made recently in the following two lines of research in the intersection of AI and game theory: (1) the computation of optimal strategies to commit to (Stackelberg strategies), and (2) the computation of correlated equilibria of stochastic games. In this paper, we unite these two lines of research by studying the computation of Stackelberg strategies in stochastic games. We provide theoretical results on the value of being able to commit and the value of being able to correlate, as well as complexity results about computing Stackelberg strategies in stochastic games. We then modify the QPACE algorithm (MacDermed et al. 2011) to compute Stackelberg strategies, and provide experimental results
Computing optimal strategies to commit to in stochastic games
Significant progress has been made recently in the following two lines of research in the intersection of AI and game theory: (1) the computation of optimal strategies to commit to (Stackelberg strategies), and (2) the computation of correlated equilibria of stochastic games. In this paper, we unite these two lines of research by studying the computation of Stackelberg strategies in stochastic games. We provide theoretical results on the value of being able to commit and the value of being able to correlate, as well as complexity results about computing Stackelberg strategies in stochastic games. We then modify the QPACE algorithm (MacDermed et al. 2011) to compute Stackelberg strategies, and provide experimental results.