Abstract-This tool paper introduces COSMOS, a statistical model checker for the Hybrid Automata Stochastic Logic (HASL). HASL employs Linear Hybrid Automata (LHA), a generalization of Deterministic Timed Automata (DTA), to describe accepting execution paths of a Discrete Event Stochastic Process (DESP), a class of stochastic models which includes, but is not limited to, Markov chains. As a result HASL verification turns out to be a unifying framework where sophisticated temporal reasoning is naturally blended with elaborate reward-based analysis. COSMOS takes as input a DESP (described in terms of a Generalized Stochastic Petri Net), an LHA and an expression Z representing the quantity to be estimated. It returns a confidence interval estimation of Z. COSMOS is written in C++ and is freely available to the research community.

We introduce the Hybrid Automata Stochastic Logic (HASL), a new temporal logic formalism for the verification of discrete event stochastic processes (DESP). HASL employs Linear Hybrid Automata (LHA) as machineries to select prefixes of relevant execution paths of a DESP D. The advantage with LHA is that rather elaborate information can be collected on-the-fly during path selection, providing the user with a powerful means to express sophisticated measures. A formula of HASL consists of an LHA A and an expression Z referring to moments of path random variables. A simulation-based statistical engine is employed to obtained a confidence-interval estimate of the expected value of Z. In essence HASL provide a unifying verification framework where sophisticated temporal reasoning is naturally blended with elaborate reward-based analysis. We illustrate the HASL approach by means of some examples and a discussion about its expressivity. We also provide empirical evidence obtained through COSMOS, a prototype software tool for HASL verification.

Abstract-Flexible Manufacturing Systems (FMS) are amongst the most studied types of systems, however due to their increasing complexity, there is still room for improvement in their modeling and analysis. In this paper we consider the design and the analysis of stochastic models of FMS in two complementary respects. First we describe a (stochastic) Petri Nets based compositional framework which enables to model an FMS by combination of an arbitrary number of basic components. Second we demonstrate how classical transient-analysis of manufacturing systems, including reliability and performability analysis, can be enriched by application of a novel, sophisticated stochastic logic, namely the Hybrid Automata Stochastic Logic (HASL). We demonstrate the proposed methodology on an FMS example.

Abstract-Censored Markov chains (CMC) allow to represent the conditional behavior of a system within a subset of observed states. They provide a theoretical framework to study the truncation of a discrete-time Markov chain when the generation of the state-space is too hard or when the number of states is too large. However, the stochastic matrix of a CMC may be difficult to obtain. Dayar et al. (2006) have proposed an algorithm, called DPY, that computes a stochastic bounding matrix for a CMC with a smaller complexity with only a partial knowledge of the chain. We prove that this algorithm is optimal for the information they take into account. We also show how some additional knowledge on the chain can improve stochastic bounds for CMC.