An Algorithm for Probabilistic Alternating Simulation

  • Chenyi Zhang
  • Jun Pang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7147)

Abstract

In probabilistic game structures, probabilistic alternating simulation (PA-simulation) relations preserve formulas defined in probabilistic alternating-time temporal logic with respect to the behaviour of a subset of players. We propose a partition based algorithm for computing the largest PA-simulation. It is to our knowledge the first such algorithm that works in polynomial time. Our solution extends the generalised coarsest partition problem (GCPP) to a game-based setting with mixed strategies. The algorithm has higher complexities than those in the literature for non-probabilistic simulation and probabilistic simulation without mixed actions, but slightly improves the existing result for computing probabilistic simulation with respect to mixed actions.

Keywords

Linear Programming Problem Mixed Strategy Stochastic Game Mixed Action Simulation Relation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chenyi Zhang
    • 1
    • 2
  • Jun Pang
    • 3
  1. 1.University of QueenslandBrisbaneAustralia
  2. 2.University of New South WalesSydneyAustralia
  3. 3.University of LuxembourgLuxembourg

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