Implicit data structures for logic and stochastic systems analysis

  • Authors:
  • Gianfranco Ciardo;Andrew S. Miner

  • Affiliations:
  • University of California, Riverside;Iowa State University

  • Venue:
  • ACM SIGMETRICS Performance Evaluation Review
  • Year:
  • 2005
  • Hierarchical Set Decision Diagrams and Regular Models

    TACAS '09 Proceedings of the 15th International Conference on Tools and Algorithms for the Construction and Analysis of Systems: Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2009,

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Abstract

Both logic and stochastic analysis have strong theoretical underpinnings, but they have been traditionally relegated to separate areas of computer science, the former focusing on logic and discrete algorithms, the latter on exact or approximate numerical methods. In the last few years, though, there has been a convergence of research in these two areas, due to the realization that data structures used in one area can benefit the other and that, by merging the goals of the two areas, a more integrated approach to system analysis can be derived. In this paper, we describe some of the beneficial interactions between the two, and some of the research challenges ahead.