Stochastic ω-Regular Games

We study games played on graphs with omega-regular conditions specified as parity, Rabin, Streett or Muller conditions. These games have applications in the verification, synthesis, modeling, testing, and compatibility checking of reactive systems. Important distinctions between graph games are as follows: (a) turn-based vs. concurrent games, depending on whether at a state of the game only a single player makes a move, or players make moves simultaneously; (b) deterministic vs. stochastic, depending on whether the transition function is a deterministic or a probabilistic function over successor states; and (c) zero-sum vs. non-zero-sum, depending on whether the objectives of the players are strictly conflicting or not. We establish that the decision problem for turn-based stochastic zero-sum games with Rabin, Streett, and Muller objectives are NP-complete, coNP-complete, and PSPACE-complete, respectively, substantially improving the previously known 3EXPTIME bound. We also present strategy improvement style algorithms for turn-based stochastic Rabin and Streett games. In the case of concurrent stochastic zero-sum games with parity objectives we obtain a PSPACE bound, again improving the previously known 3EXPTIME bound. As a consequence, concurrent stochastic zero-sum games with Rabin, Streett, and Muller objectives can be solved in EXPSPACE, improving the previously known 4EXPTIME bound. We also present an elementary and combinatorial proof of the existence of memoryless \epsilon-optimal strategies in concurrent stochastic games with reachability objectives, for all real \epsilon>0, where an \epsilon-optimal strategy achieves the value of the game with in \epsilon against all strategies of the opponent. We also use the proof techniques to present a strategy improvement style algorithm for concurrent stochastic reachability games. We then go beyond \omega-regular objectives and study the complexity of an important class of quantitative objectives, namely, limit-average objectives. In the case of limit-average games, the states of the graph is labeled with rewards and the goal is to maximize the long-run average of the rewards. We show that concurrent stochastic zero-sum games with limit-average objectives can be solved in EXPTIME. Finally, we introduce a new notion of equilibrium, called secure equilibrium, in non-zero-sum games which captures the notion of conditional competitiveness. We prove the existence of unique maximal secure equilibrium payoff profiles in turn-based deterministic games, and present algorithms to compute such payoff profiles. We also show how the notion of secure equilibrium extends the assume-guarantee style of reasoning in the game theoretic framework.