Abstract The article compares two different approaches of incorporating probability into coalition logics. One is based on the semantics games with stochastic transitions and other failures. work gives an example a non-trivial property power for first approach complete axiomatization second approach. It turns out that logical properties modality under depend whether modal language allows empty ...

We present a new algorithm for polynomial time learning of optimal behavior in stochastic games. This algorithm incorporates and integrates important recent results of Kearns and Singh 5] in reinforcement learning and of Monderer and Tennenholtz 7] in repeated games. In stochastic games, the agent must cope with the existence of an adversary whose actions can be arbitrary. In particular, this a...

One of the recurring themes in the theory of stochastic games is the interplay between discounted games and mean-payoff games. This culminates in the seminal paper of Mertens and Neyman [12] showing that mean-payoff games have a value and this value is the limit of the values of discounted games when the discount factor tends to 1. Note however that optimal strategies in both games are very dif...

Many real-world problems modeled by stochastic games have huge state and/or action spaces, leading to the well-known curse of dimensionality. The complexity of the analysis of large-scale systems is dramatically reduced by exploiting mean field limit and dynamical system viewpoints. Under regularity assumptions and specific time-scaling techniques, the evolution of the mean field limit can be e...

Stochastic (a.k.a. Markov) Games pose many unsolved problems in Game Theory. One class of stochastic games that is better understood is that of Common Interest Stochastic Games (CISG). CISGs form an interesting class of multi-agent settings where the distributed nature of the systems, rather than adverserial behavior, is the main challenge to efficient learning. In this paper we examine three d...

Zero-sum stochastic games provide a formalism to study competitive sequential interactions between two agents with diametrically opposing goals and evolving state. A solution to such games with discrete state was presented by Littman (Littman, 1994). The continuous state version of this game remains unsolved. In many instances continuous state solutions require nonlinear optimisation, a problem...

In this paper a selection theory for stochastic games is developed. The theory itself is based on the ideas of Harsanyi and Selten to select equilibria for games in standard form. We introduce several possible definitions for the stochastic tracing procedure, an extension of the linear tracing procedure to the class of stochastic games. We analyze the properties of these alternative definitions...

Game theory [1] is a formalism for the study of competitive interaction in the rich spectrum of relationships ranging between conflict and cooperation. Originally conceived as a mathematical foundation of economics, it proved its robustness by providing new techniques and insights in logic and set theory [15, 13], evolutionary and population biology [22], auction design and implementation, the ...

Non-cooperative decision-making problems in a decentralized supply chain can be characterized and studied using a stochastic game model. In an earlier paper, the authors developed a methodology that uses machine learning for finding (near) optimal policies for non-zero sum stochastic games, and applied their methodology on an N-retailer and W-warehouse inventory-planning problem. The focus of t...

The Global Newton Method for games in normal form and in extensive form is shown to have a natural extension to computing Markov-perfect equilibria of stochastic games. GLOBAL NEWTON METHOD FOR STOCHASTIC GAMES SRIHARI GOVINDAN AND ROBERT WILSON Abstract. The Global Newton Method for games in normal form and in extensive form is shown to have a natural extension to computing Markov-perfect equi...