Quantitative multi-objective verification for probabilistic systems

  • Authors:
  • Vojtěch Forejt;Marta Kwiatkowska;Gethin Norman;David Parker;Hongyang Qu

  • Affiliations:
  • Oxford University Computing Laboratory, Parks Road, Oxford, UK;Oxford University Computing Laboratory, Parks Road, Oxford, UK;School of Computing Science, University of Glasgow, Glasgow, UK;Oxford University Computing Laboratory, Parks Road, Oxford, UK;Oxford University Computing Laboratory, Parks Road, Oxford, UK

  • Venue:
  • TACAS'11/ETAPS'11 Proceedings of the 17th international conference on Tools and algorithms for the construction and analysis of systems: part of the joint European conferences on theory and practice of software
  • Year:
  • 2011

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Abstract

We present a verification framework for analysing multiple quantitative objectives of systems that exhibit both nondeterministic and stochastic behaviour. These systems are modelled as probabilistic automata, enriched with cost or reward structures that capture, for example, energy usage or performance metrics. Quantitative properties of these models are expressed in a specification language that incorporates probabilistic safety and liveness properties, expected total cost or reward, and supports multiple objectives of these types. We propose and implement an efficient verification framework for such properties and then present two distinct applications of it: firstly, controller synthesis subject to multiple quantitative objectives; and, secondly, quantitative compositional verification. The practical applicability of both approaches is illustrated with experimental results from several large case studies.