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.