Distributed Parametric and Statistical Model Checking

Statistical Model Checking (SMC) is a trade-off between testing and formal verification. The core idea of the approach is to conduct some simulations of the system and verify if they satisfy some given property. In this paper we show that SMC is easily parallelizable on a master/slaves architecture by introducing a series of algorithms that scale almost linearly with respect to the number of slave computers. Our approach has been implemented in the UPPAAL SMC toolset and applied on non-trivial case studies.Comment: In Proceedings PDMC 2011, arXiv:1111.006

Statistical Model Checking for Stochastic Hybrid Systems

This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings.Comment: In Proceedings HSB 2012, arXiv:1208.315

Planning and Verification for Stochastic Processes with Asynchronous Events

Abstract We consider a general model of stochastic discrete event systems with asynchronous events, and propose to develop efficient algorithms for verification and control of such systems

Policy Generation for Continuous-time Stochastic Domains with Concurrency

for planning with concurrency in continuous-time stochastic domains. Our contribution is a set of concrete techniques for policy generation, failure analysis, and repair. These techniques have been implemented in TEMPASTIC,anoveltem- poral probabilistic planner, and we demonstrate the performance of the planner on two variations of a transportation domain with concurrent actions and exogenous events. TEM- PASTIC makes use of a deterministic temporal planner to generate initial policies. Policies are represented using decision trees, and we use incremental decision tree induction to efficiently incorporate changes suggested by the failure analysis