Learning regular sets from queries and counterexamples
Information and Computation
A polynomial-time algorithm for the equivalence of probabilistic automata
SIAM Journal on Computing
Learning Probabilistic Automata and Markov Chains via Queries
Machine Learning
Modeling and verification of randomized distributed real-time systems
Modeling and verification of randomized distributed real-time systems
Learning Behaviors of Automata from Multiplicity and Equivalence Queries
SIAM Journal on Computing
Compositional Methods for Probabilistic Systems
CONCUR '01 Proceedings of the 12th International Conference on Concurrency Theory
Principles of Model Checking (Representation and Mind Series)
Principles of Model Checking (Representation and Mind Series)
Learning to divide and conquer: applying the L* algorithm to automate assume-guarantee reasoning
Formal Methods in System Design
Counterexample Generation in Probabilistic Model Checking
IEEE Transactions on Software Engineering
Compositional Verification of Probabilistic Systems Using Learning
QEST '10 Proceedings of the 2010 Seventh International Conference on the Quantitative Evaluation of Systems
Quantitative multi-objective verification 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
Assume-Guarantee verification for probabilistic systems
TACAS'10 Proceedings of the 16th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Learning Probabilistic Systems from Tree Samples
LICS '12 Proceedings of the 2012 27th Annual IEEE/ACM Symposium on Logic in Computer Science
Assume-guarantee abstraction refinement for probabilistic systems
CAV'12 Proceedings of the 24th international conference on Computer Aided Verification
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We present novel techniques for automated compositional verification of synchronous probabilistic systems. First, we give an assume-guarantee framework for verifying probabilistic safety properties of systems modelled as discrete-time Markov chains. Assumptions about system components are represented as probabilistic finite automata (PFAs) and the relationship between components and assumptions is captured by weak language inclusion. In order to implement this framework, we develop a semi-algorithm to check language inclusion for PFAs and a new active learning method for PFAs. The latter is then used to automatically generate assumptions for compositional verification.