Titlebook: Quantitative Evaluation of Systems; 16th International C David Parker,Verena Wolf Conference proceedings 2019 Springer Nature Switzerland A

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Model Checking Constrained Markov Reward Models with Uncertaintieslysis are (i) probabilistic bisimilarity, and (ii) specifications expressed as probabilistic reward CTL formulae..We consider two extensions of the notion of MRM, namely (a) ., i.e., MRMs with rewards parametric on a set variables subject to some constraints, and (b) ., i.e., MRMs with rewards model

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A Modest Approach to Modelling and Checking Markov Automata paper, we present extensions to the . language and the . model checker to describe and analyse Markov automata models. . is an expressive high-level language with roots in process algebra that allows large models to be specified in a succinct, modular way. We explain its use for Markov automata and

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Strategy Representation by Decision Trees with Linear Classifierssm. The class of .-regular winning conditions; e.g., safety, reachability, liveness, parity conditions; provides a robust and expressive specification formalism for properties that arise in analysis of reactive systems. The resolutions of nondeterminism in games and MDPs are represented as strategie

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Neural Network Precision Tunings specially important for neural networks used in embedded systems. Unfortunately, neural networks are very sensitive to the precision in which they have been trained and changing this precision generally degrades the quality of their answers. In this article, we introduce a new technique to tune th

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