Luca Bortolussi ; Guido Sanguinetti - Learning and Designing Stochastic Processes from Logical Constraints

lmcs:1563 - Logical Methods in Computer Science, June 1, 2015, Volume 11, Issue 2 - https://doi.org/10.2168/LMCS-11(2:3)2015
Learning and Designing Stochastic Processes from Logical ConstraintsArticle

Authors: Luca Bortolussi ORCID; Guido Sanguinetti

    Stochastic processes offer a flexible mathematical formalism to model and reason about systems. Most analysis tools, however, start from the premises that models are fully specified, so that any parameters controlling the system's dynamics must be known exactly. As this is seldom the case, many methods have been devised over the last decade to infer (learn) such parameters from observations of the state of the system. In this paper, we depart from this approach by assuming that our observations are {\it qualitative} properties encoded as satisfaction of linear temporal logic formulae, as opposed to quantitative observations of the state of the system. An important feature of this approach is that it unifies naturally the system identification and the system design problems, where the properties, instead of observations, represent requirements to be satisfied. We develop a principled statistical estimation procedure based on maximising the likelihood of the system's parameters, using recent ideas from statistical machine learning. We demonstrate the efficacy and broad applicability of our method on a range of simple but non-trivial examples, including rumour spreading in social networks and hybrid models of gene regulation.


    Volume: Volume 11, Issue 2
    Published on: June 1, 2015
    Submitted on: December 29, 2013
    Keywords: Computer Science - Systems and Control
    Funding:
      Source : OpenAIRE Graph
    • A Quantitative Approach to Management and Design of Collective and Adaptive Behaviours; Funder: European Commission; Code: 600708
    • Machine learning for computational science: statistical and formal modelling of biological systems; Funder: European Commission; Code: 306999

    2 Documents citing this article

    Consultation statistics

    This page has been seen 1254 times.
    This article's PDF has been downloaded 962 times.