Publication

Stochastic limit-average games are in EXPTIME

2008
Journal Articles

Abstract

The value of a finite-state two-player zero-sum stochastic game with limit-average payoff can be approximated to within e in time exponential in a polynomial in the size of the game times polynomial in logarithmic in 1/epsilon, for all epsilon > 0.

Official source

https://infoscience.epfl.ch/entities/publication/a1757dd4-2a1a-4198-952b-dcc87018451d

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2008
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/a1757dd4-2a1a-4198-952b-dcc87018451d

About this result

Related concepts (10)

Cooperative game theory

In game theory, a cooperative game (or coalitional game) is a game with competition between groups of players ("coalitions") due to the possibility of external enforcement of cooperative behavior (e.g. through contract law). Those are opposed to non-cooperative games in which there is either no possibility to forge alliances or all agreements need to be self-enforcing (e.g. through credible threats). Cooperative games are often analysed through the framework of cooperative game theory, which focuses on predicting which coalitions will form, the joint actions that groups take and the resulting collective payoffs.

Markov chain

A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. Informally, this may be thought of as, "What happens next depends only on the state of affairs now." A countably infinite sequence, in which the chain moves state at discrete time steps, gives a discrete-time Markov chain (DTMC). A continuous-time process is called a continuous-time Markov chain (CTMC).

Stochastic process

In probability theory and related fields, a stochastic (stəˈkæstɪk) or random process is a mathematical object usually defined as a sequence of random variables, where the index of the sequence has the interpretation of time. Stochastic processes are widely used as mathematical models of systems and phenomena that appear to vary in a random manner. Examples include the growth of a bacterial population, an electrical current fluctuating due to thermal noise, or the movement of a gas molecule.

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