8,329 research outputs found

    Approaching the Coverability Problem Continuously

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    The coverability problem for Petri nets plays a central role in the verification of concurrent shared-memory programs. However, its high EXPSPACE-complete complexity poses a challenge when encountered in real-world instances. In this paper, we develop a new approach to this problem which is primarily based on applying forward coverability in continuous Petri nets as a pruning criterion inside a backward coverability framework. A cornerstone of our approach is the efficient encoding of a recently developed polynomial-time algorithm for reachability in continuous Petri nets into SMT. We demonstrate the effectiveness of our approach on standard benchmarks from the literature, which shows that our approach decides significantly more instances than any existing tool and is in addition often much faster, in particular on large instances.Comment: 18 pages, 4 figure

    Learning Petri net models of non-linear gene interactions

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    Understanding how an individual's genetic make-up influences their risk of disease is a problem of paramount importance. Although machine-learning techniques are able to uncover the relationships between genotype and disease, the problem of automatically building the best biochemical model or “explanation” of the relationship has received less attention. In this paper, I describe a method based on random hill climbing that automatically builds Petri net models of non-linear (or multi-factorial) disease-causing gene–gene interactions. Petri nets are a suitable formalism for this problem, because they are used to model concurrent, dynamic processes analogous to biochemical reaction networks. I show that this method is routinely able to identify perfect Petri net models for three disease-causing gene–gene interactions recently reported in the literature

    Analysis of signalling pathways using the prism model checker

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    We describe a new modelling and analysis approach for signal transduction networks in the presence of incomplete data. We illustrate the approach with an example, the RKIP inhibited ERK pathway [1]. Our models are based on high level descriptions of continuous time Markov chains: reactions are modelled as synchronous processes and concentrations are modelled by discrete, abstract quantities. The main advantage of our approach is that using a (continuous time) stochastic logic and the PRISM model checker, we can perform quantitative analysis of queries such as if a concentration reaches a certain level, will it remain at that level thereafter? We also perform standard simulations and compare our results with a traditional ordinary differential equation model. An interesting result is that for the example pathway, only a small number of discrete data values is required to render the simulations practically indistinguishable

    Internet enabled modelling of extended manufacturing enterprises using the process based techniques

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    The paper presents the preliminary results of an ongoing research project on Internet enabled process-based modelling of extended manufacturing enterprises. It is proposed to apply the Open System Architecture for CIM (CIMOSA) modelling framework alongside with object-oriented Petri Net models of enterprise processes and object-oriented techniques for extended enterprises modelling. The main features of the proposed approach are described and some components discussed. Elementary examples of object-oriented Petri Net implementation and real-time visualisation are presented

    Model-based dependability analysis : state-of-the-art, challenges and future outlook

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    Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis
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