Learning regular sets from queries and counterexamples
Information and Computation
Proceedings of the Fourth Annual Symposium on Logic in computer science
In transition from global to modular temporal reasoning about programs
Logics and models of concurrent systems
Learning Probabilistic Automata and Markov Chains via Queries
Machine Learning
Learning functions represented as multiplicity automata
Journal of the ACM (JACM)
Deciding bisimilarity and similarity for probabilistic processes
Journal of Computer and System Sciences
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Stochastic Inference of Regular Tree Languages
Machine Learning
Probabilistic simulations for probabilistic processes
Nordic Journal of Computing
Compositional Methods for Probabilistic Systems
CONCUR '01 Proceedings of the 12th International Conference on Concurrency Theory
An algebraic definition of simulation between programs
An algebraic definition of simulation between programs
Automated assumption generation for compositional verification
Formal Methods in System Design
Learning to divide and conquer: applying the L* algorithm to automate assume-guarantee reasoning
Formal Methods in System Design
Learning Minimal Separating DFA's for Compositional Verification
TACAS '09 Proceedings of the 15th International Conference on Tools and Algorithms for the Construction and Analysis of Systems: Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2009,
A counterexample-guided abstraction-refinement framework for markov decision processes
ACM Transactions on Computational Logic (TOCL)
Automated learning of probabilistic assumptions for compositional reasoning
FASE'11/ETAPS'11 Proceedings of the 14th international conference on Fundamental approaches to software engineering: part of the joint European conferences on theory and practice of software
Learning-based compositional verification for synchronous probabilistic systems
ATVA'11 Proceedings of the 9th international conference on Automated technology for verification and analysis
Learning Probabilistic Automata for Model Checking
QEST '11 Proceedings of the 2011 Eighth International Conference on Quantitative Evaluation of SysTems
Automated assume-guarantee reasoning for simulation conformance
CAV'05 Proceedings of the 17th international conference on Computer Aided Verification
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
Assume-guarantee abstraction refinement for probabilistic systems
CAV'12 Proceedings of the 24th international conference on Computer Aided Verification
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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We consider the problem of learning a non-deterministic probabilistic system consistent with a given finite set of positive and negative tree samples. Consistency is defined with respect to strong simulation conformance. We propose learning algorithms that use traditional and a new stochastic state-space partitioning, the latter resulting in the minimum number of states. We then use them to solve the problem of active learning, that uses a knowledgeable teacher to generate samples as counterexamples to simulation equivalence queries. We show that the problem is undecidable in general, but that it becomes decidable under a suitable condition on the teacher which comes naturally from the way samples are generated from failed simulation checks. The latter problem is shown to be undecidable if we impose an additional condition on the learner to always conjecture a minimum state hypothesis. We therefore propose a semi-algorithm using stochastic partitions. Finally, we apply the proposed (semi-) algorithms to infer intermediate assumptions in an automated assume-guarantee verification framework for probabilistic systems.