Projects per year
Abstract
Stochastic models such as ContinuousTime Markov Chains (CTMC) and Stochastic Hybrid Automata (SHA) are powerful formalisms to model and to reason about the dynamics of biological systems, due to their ability to capture the stochasticity inherent in biological processes. A classical question in formal modelling with clear relevance to biological modelling is the model checking problem, i.e. calculate the probability that a behaviour, expressed for instance in terms of a certain temporal logic formula, may occur in a given stochastic process. However, one may not only be interested in the notion of satisfiability, but also in the capacity of a system to maintain a particular emergent behaviour unaffected by the perturbations, caused e.g. from extrinsic noise, or by possible small changes in the model parameters. To address this issue, researchers from the verification community have recently proposed several notions of robustness for temporal logic providing suitable definitions of distance between a trajectory of a (deterministic) dynamical system and the boundaries of the set of trajectories satisfying the property of interest. The contributions of this paper are twofold. First, we extend the notion of robustness to stochastic systems, showing that this naturally leads to a distribution of robustness degrees. By discussing three examples, we show how to approximate the distribution of the robustness degree and the average robustness. Secondly, we show how to exploit this notion to address the system design problem, where the goal is to optimise some control parameters of a stochastic model in order to maximise robustness of the desired specifications.
Original language  English 

Pages (fromto)  325 
Number of pages  23 
Journal  Theoretical Computer Science 
Volume  587 
DOIs  
Publication status  Published  1 Jul 2015 
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Projects
 2 Finished

QUANTICOL  A Quantitative Approach to Management and Design of Collective and Adaptive Behaviours (RTD)
1/04/13 → 31/03/17
Project: Research

MLCS  Machine learning for computational science statistical and formal modeling of biological systems
1/10/12 → 30/09/17
Project: Research