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Abstract

Bisimulations enjoy numerous applications in the analysis of labeled transition systems. Many of these applications are based on two central observations: first, bisimilar systems satisfy the same branching-time properties; second, bisimilarity can be checked efficiently for finite-state systems. The local character of bisimulation, however, makes it difficult to address liveness concerns. Indeed, the definitions of fair bisimulation that have been proposed in the literature sacrifice locality, and with it, also efficient checkability. We put forward a new definition of fair bisimulation which does not suffer from this drawback.

The bisimilarity of two systems can be viewed in terms of a game played between a protagonist and an adversary. In each step of the infinite bisimulation game, the adversary chooses one system, makes a move, and the protagonist matches it with a move of the other system. Consistent with this game-based view, we call two fair transition systems bisimilar if in the bisimulation game, the infinite path produced in the first system is fair iff the infinite path produced in the second system is fair.

We show that this notion of fair bisimulation enjoys the following properties. First, fairly bisimilar systems satisfy the same formulas of the logics Fair-AFMC (the fair alternation-free μ-calculus) and Fair-CTL*. Therefore, fair bisimulations can serve as property-preserving abstractions for these logics and weaker ones, such as Fair-CTL and LTL. Indeed, Fair-AFMC provides an exact logical characterization of fair bisimilarity. Second, it can be checked in time polynomial in the number of states if two systems are fairly bisimilar. This is in stark contrast to all trace-based equivalences, which are traditionally used for addressing liveness but require exponential time for checking.

Keywords

Binary Relation Winning Strategy Label Transition System Kripke Structure Query Logic 
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 2000

Authors and Affiliations

  • Thomas A. Henzinger
    • 1
  • Sriram K. Rajamani
    • 2
  1. 1.University of California at BerkeleyBerkeley
  2. 2.Microsoft ResearchUSA

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