Algebraic approaches to graph transformation. Part I: basic concepts and double pushout approach
Handbook of graph grammars and computing by graph transformation
Maude: specification and programming in rewriting logic
Theoretical Computer Science - Rewriting logic and its applications
Fundamentals of Algebraic Graph Transformation (Monographs in Theoretical Computer Science. An EATCS Series)
Game-based Abstraction for Markov Decision Processes
QEST '06 Proceedings of the 3rd international conference on the Quantitative Evaluation of Systems
PMaude: Rewrite-based Specification Language for Probabilistic Object Systems
Electronic Notes in Theoretical Computer Science (ENTCS)
Modeling and verification of cooperative self-adaptive mechatronic systems
Proceedings of the 12th Monterey conference on Reliable systems on unreliable networked platforms
Henshin: advanced concepts and tools for in-place EMF model transformations
MODELS'10 Proceedings of the 13th international conference on Model driven engineering languages and systems: Part I
PRISM 4.0: verification of probabilistic real-time systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Formal verification and simulation for performance analysis for probabilistic broadcast protocols
ADHOC-NOW'06 Proceedings of the 5th international conference on Ad-Hoc, Mobile, and Wireless Networks
Model checking dynamic states in GROOVE
SPIN'06 Proceedings of the 13th international conference on Model Checking Software
Stochastic Graph Transformation Systems
Fundamenta Informaticae - SPECIAL ISSUE ON ICGT 2004
Graph Transformation with Time
Fundamenta Informaticae - The First International Conference on Graph Transformation (ICGT 2002)
A survey and comparison of transformation tools based on the transformation tool contest
Science of Computer Programming
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In the recent years, extensions of graph transformation systems with quantitative properties, such as real-time and stochastic behavior received considerable attention. In this paper, we describe the new quantitative modeling approach of probabilistic graph transformation systems (PGTSs) which incorporate probabilistic behavior into graph transformation systems. Among other applications, PGTSs permit to model randomized protocols in distributed and mobile systems, and systems with on-demand probabilistic failures, such as message losses in unreliable communication media. We define the semantics of PGTSs in terms of Markov decision processes and employ probabilistic model checking for the quantitative analysis of finite-state PGTS models. We present tool support using Henshin and Prism for the modeling and analysis and discuss a probabilistic broadcast case study for wireless sensor networks.