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 ...
Joshua Letchford, Liam MacDermed, Vincent Conitzer...
We study the computational complexity of basic decision problems for one-counter simple stochastic games (OC-SSGs), under various objectives. OC-SSGs are 2-player turn-based stoch...
Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on ...
Abstract. In this paper we consider a class of hybrid stochastic games with the piecewise openloop information structure. These games are indexed over a parameter which represents...
The value of a finite-state two-player zero-sum stochastic game with limit-average payoff can be approximated to within in time exponential in a polynomial in the size of the gam...
Krishnendu Chatterjee, Rupak Majumdar, Thomas A. H...
Stochastic games generalize Markov decision processes MDPs to a multiagent setting by allowing the state transitions to depend jointly on all player actions, and having rewards de...
Michael J. Kearns, Yishay Mansour, Satinder P. Sin...
This paper investigates the problem of policy learning in multiagent environments using the stochastic game framework, which we briefly overview. We introduce two properties as de...
Abstract. We define the first nontrivial polynomially recognizable subclass of P-matrix Generalized Linear Complementarity Problems (GLCPs) with a subexponential pivot rule. No suc...
We introduce Recursive Markov Decision Processes (RMDPs) and Recursive Simple Stochastic Games (RSSGs), which are classes of (finitely presented) countable-state MDPs and zero-su...
We propose axiomatizing some stochastic games, in a continuous state space setting, using continuous belief functions, resp. plausibilities, instead of measures. Then, stochastic g...