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Learning and Designing Stochastic Processes from Logical Constraints

  • Luca Bortolussi
  • Guido Sanguinetti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8054)

Abstract

Continuous time Markov Chains (CTMCs) are a convenient mathematical model for a broad range of natural and computer systems. As a result, they have received considerable attention in the theoretical computer science community, with many important techniques such as model checking being now mainstream. However, most methodologies start with an assumption of complete specification of the CTMC, in terms of both initial conditions and parameters. While this may be plausible in some cases (e.g. small scale engineered systems) it is certainly not valid nor desirable in many cases (e.g. biological systems), and it does not lead to a constructive approach to rational design of systems based on specific requirements. Here we consider the problems of learning and designing CTMCs from observations/ requirements formulated in terms of satisfaction of temporal logic formulae. We recast the problem in terms of learning and maximising an unknown function (the likelihood of the parameters) which can be numerically estimated at any value of the parameter space (at a non-negligible computational cost). We adapt a recently proposed, provably convergent global optimisation algorithm developed in the machine learning community, and demonstrate its efficacy on a number of non-trivial test cases.

Keywords

Model Check Logical Constraint Continuous Time Markov Chain Infected Node Statistical Model Check 
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 2013

Authors and Affiliations

  • Luca Bortolussi
    • 1
    • 2
  • Guido Sanguinetti
    • 3
    • 4
  1. 1.Department of Mathematics and GeosciencesUniversity of TriesteItaly
  2. 2.CNR/ISTIPisaItaly
  3. 3.School of InformaticsUniversity of EdinburghUK
  4. 4.SynthSys, Centre for Synthetic and Systems BiologyUniversity of EdinburghUK

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