A Novel Stochastic Game Via the Quantitative μ-calculus

The quantitative μ-calculus qMμ extends the applicability of Kozen's standard μ-calculus [D. Kozen, Results on the propositional μ-calculus, Theoretical Computer Science 27 (1983) 333–354] to probabilistic systems. Subsequent to its introduction [C. Morgan, and A. McIver, A probabilistic temporal calculus based on expectations, in: L. Groves and S. Reeves, editors, Proc. Formal Methods Pacific '97 (1997), available at [PSG, Probabilistic Systems Group: Collected reports, http://web.comlab.ox.ac.uk/oucl/research/areas/probs/bibliography.html]; also appears at [A. McIver, and C. Morgan, “Abstraction, Refinement and Proof for Probabilistic Systems,” Technical Monographs in Computer Science, Springer, New York, 2005, Chap. 9], M. Huth, and M. Kwiatkowska, Quantitative analysis and model checking, in: Proceedings of 12th annual IEEE Symposium on Logic in Computer Science, 1997] it has been developed by us [A. McIver, and C. Morgan, Games, probability and the quantitative μ-calculus qMu, in: Proc. LPAR, LNAI 2514 (2002), pp. 292–310, revised and expanded at [A. McIver, and C. Morgan, Results on the quantitative μ-calculus qMμ (2005), to appear in ACM TOCL]; also appears at [A. McIver, and C. Morgan, “Abstraction, Refinement and Proof for Probabilistic Systems,” Technical Monographs in Computer Science, Springer, New York, 2005, Chap. 11], A. McIver, and C. Morgan, “Abstraction, Refinement and Proof for Probabilistic Systems,” Technical Monographs in Computer Science, Springer, New York, 2005, A. McIver, and C. Morgan, Results on the quantitative μ-calculus qMμ (2005), to appear in ACM TOCL] and by others [L. de Alfaro, and R. Majumdar, Quantitative solution of omega-regular games, Journal of Computer and System Sciences 68 (2004) 374–397]. Beyond its natural application to define probabilistic temporal logic [C. Morgan, and A. McIver, An expectation-based model for probabilistic temporal logic, Logic Journal of the IGPL 7 (1999), pp. 779–804, also appears at [A. McIver, and C. Morgan, “Abstraction, Refinement and Proof for Probabilistic Systems,” Technical Monographs in Computer Science, Springer, New York, 2005, Chap.10]], there are a number of other areas that benefit from its use.One application is stochastic two-player games, and the contribution of this paper is to depart from the usual notion of “absolute winning conditions” and to introduce a novel game in which players can “draw”.The extension is motivated by examples based on economic games: we propose an extension to qMμ so that they can be specified; we show that the extension can be expressed via a reduction to the original logic; and, via that reduction, we prove that the players can play optimally in the extended game using memoryless strategies.