some image logo

HOME

SEARCH

CURRENT ISSUE

REGULAR ISSUES

   Volume 1 (2005)

   Volume 2 (2006)

   Volume 3 (2007)

   Volume 4 (2008)

   Volume 5 (2009)

   Volume 6 (2010)

   Volume 7 (2011)

   Volume 8 (2012)

   Volume 9 (2013)

   Volume 10 (2014)

   Volume 11 (2015)

      Issue 1

      Issue 2

      Issue 3

      Issue 4

   Volume 12 (2016)

   Volume 13 (2017)

SPECIAL ISSUES

SURVEY ARTICLES

AUTHORS

ABOUT

SERVICE

LOGIN

FAQ

SUPPORT

CONTACT

VOLUME 11, ISSUE 2, PAPER 3


Learning and Designing Stochastic Processes from Logical Constraints

©Luca Bortolussi, University of Trieste
©Guido Sanguinetti, University of Edinmburgh

Abstract
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.

Publication date: June 1, 2015

Full Text: PDF | PostScript
DOI: 10.2168/LMCS-11(2:3)2015

Hit Counts: 4020

Creative Commons