ACM Transactions on Computer Systems (TOCS)
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APN 90 Proceedings on Advances in Petri nets 1990
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Electronic Notes in Theoretical Computer Science (ENTCS)
Partial Order Reduction for Probabilistic Branching Time
Electronic Notes in Theoretical Computer Science (ENTCS)
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IEEE Transactions on Dependable and Secure Computing
Concurrency and composition in a stochastic world
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On Probabilistic Automata in Continuous Time
LICS '10 Proceedings of the 2010 25th Annual IEEE Symposium on Logic in Computer Science
Confluence reduction for probabilistic systems
TACAS'11/ETAPS'11 Proceedings of the 17th international conference on Tools and algorithms for the construction and analysis of systems: part of the joint European conferences on theory and practice of software
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The Computer Journal
On the semantics of Markov automata
ICALP'11 Proceedings of the 38th international conference on Automata, languages and programming - Volume Part II
SCOOP: A Tool for SymboliC Optimisations of Probabilistic Processes
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ACSD '12 Proceedings of the 2012 12th International Conference on Application of Concurrency to System Design
Efficient modelling and generation of Markov automata
CONCUR'12 Proceedings of the 23rd international conference on Concurrency Theory
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Markov automata are a novel formalism for specifying systems exhibiting nondeterminism, probabilistic choices and Markovian rates. Recently, the process algebra MAPA was introduced to efficiently model such systems. As always, the state space explosion threatens the analysability of the models generated by such specifications. We therefore introduce confluence reduction for Markov automata, a powerful reduction technique to keep these models small. We define the notion of confluence directly on Markov automata, and discuss how to syntactically detect confluence on the MAPA language as well. That way, Markov automata generated by MAPA specifications can be reduced on-the-fly while preserving divergence-sensitive branching bisimulation. Three case studies demonstrate the significance of our approach, with reductions in analysis time up to an order of magnitude.