Add to ORKGInternational audienceMarkov decision processes (MDP) are useful to model concurrent process optimisation problems, but verifying them with numerical methods is often intractable. Existing approximative approaches do not scale well and are limited to memoryless schedulers. Here we present the basis of scalable verification for MDPs, using an O(1) memory representation of history-dependent schedulers. We thus facilitate scalable learning techniques and the use of massively parallel verification
We present a general framework for applying machine-learning algorithms to the verification of Marko...
For concurrent probabilistic programs having process-level nondeterminism, it is often necessary to ...
Abstract. We present a general framework for applying machine-learning algo-rithms to the verificati...
International audienceMarkov decision processes (MDP) are useful to model concurrent process optimis...
International audienceMarkov decision processes (MDP) are useful to model concurrent process optimis...
Submitted to conferenceMarkov decision processes are useful models of concurrency optimisation probl...
Submitted to conferenceMarkov decision processes are useful models of concurrency optimisation probl...
We propose a simple and efficient technique that allows the application of statistical model checkin...
We propose a simple and efficient technique that allows the application of statistical model checkin...
International audienceMarkov decision processes (MDP) are useful to model optimisation problems in c...
International audienceMarkov decision processes (MDP) are useful to model optimisation problems in c...
We propose a simple and efficient technique that allows the application of statistical model checkin...
We propose a simple and efficient technique that allows the application of statistical model checkin...
Submitted to conferenceMarkov decision processes are useful models of concurrency optimisation probl...
Submitted to conferenceMarkov decision processes are useful models of concurrency optimisation probl...
We present a general framework for applying machine-learning algorithms to the verification of Marko...
For concurrent probabilistic programs having process-level nondeterminism, it is often necessary to ...
Abstract. We present a general framework for applying machine-learning algo-rithms to the verificati...
International audienceMarkov decision processes (MDP) are useful to model concurrent process optimis...
International audienceMarkov decision processes (MDP) are useful to model concurrent process optimis...
Submitted to conferenceMarkov decision processes are useful models of concurrency optimisation probl...
Submitted to conferenceMarkov decision processes are useful models of concurrency optimisation probl...
We propose a simple and efficient technique that allows the application of statistical model checkin...
We propose a simple and efficient technique that allows the application of statistical model checkin...
International audienceMarkov decision processes (MDP) are useful to model optimisation problems in c...
International audienceMarkov decision processes (MDP) are useful to model optimisation problems in c...
We propose a simple and efficient technique that allows the application of statistical model checkin...
We propose a simple and efficient technique that allows the application of statistical model checkin...
Submitted to conferenceMarkov decision processes are useful models of concurrency optimisation probl...
Submitted to conferenceMarkov decision processes are useful models of concurrency optimisation probl...
We present a general framework for applying machine-learning algorithms to the verification of Marko...
For concurrent probabilistic programs having process-level nondeterminism, it is often necessary to ...
Abstract. We present a general framework for applying machine-learning algo-rithms to the verificati...