Improved Algorithms for the Automata-Based Approach to Model-Checking

  • Laurent Doyen
  • Jean-François Raskin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4424)


We propose and evaluate new algorithms to support the automata-based approach to model-checking: algorithms to solve the universality and language inclusion problems for nondeterministic Büchi automata. To obtain those new algorithms, we establish the existence of pre-orders that can be exploited to efficiently evaluate fixed points on the automata defined during the complementation step (that we keep implicit in our approach). We evaluate the performance of our new algorithm to check for universality of Büchi automata experimentally using the random automaton model recently proposed by Tabakov and Vardi. We show that on the difficult instances of this probabilistic model, our algorithm outperforms the standard ones by several orders of magnitude. This work is an extension to the infinite words case of new algorithms for the finite words case that we and co-authors have presented in a recent paper [DDHR06].


Maximal Element Improve Algorithm Simulation Relation Emptiness Problem Language Inclusion 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Laurent Doyen
    • 1
  • Jean-François Raskin
    • 2
  1. 1.I&C, Ecole Polytechnique Fédérale de Lausanne (EPFL)Switzerland
  2. 2.CS, Université Libre de Bruxelles (ULB)Belgium

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