Abstraction of Graph Transformation Systems by Temporal Logic and Its Verification

Yamamoto, Masahiro; Tanabe, Yoshinori; Takahashi, Koichi; Hagiya, Masami

doi:10.1007/978-3-540-69149-5_57

Cited by 3 publications

(1 citation statement)

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“…This allows the level of precision to be adjusted if the current abstraction does not allow the verification of properties. [25] uses a similar approach, but abstracted nodes are characterised by satisfaction of temporal logic formulae representing some behavioural property of the concrete system. In [22], based on shape graphs introduced in [23], nodes are grouped by structural similarity with multiplicities to capture concrete representations of an abstract shape.…”

Section: Related Workmentioning

confidence: 99%

Abstraction and Training of Stochastic Graph Transformation Systems

Bapodra

Heckel

2013

Fundamental Approaches to Software Engineering

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Simulation of stochastic graph transformation systems (SGTS) allows us to analyse the model's behaviour. However, complexity of models limits our capability for analysis. In this paper, we aim to simplify models by abstraction while preserving relevant trends in their global behaviour. Based on a hierarchical graph model inspired by membrane systems, structural abstraction is achieved by "zooming out" of membranes, hiding their internal state. We use Bayesian networks representing dependencies on stochastic (input) parameters, as well as causal relationships between rules, for parameter learning and inference. We demonstrate and evaluate this process via two case studies, immunological response to a viral attack and reconfiguration in P2P networks.

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Section: Related Workmentioning

confidence: 99%