@inproceedings{c378ba53e4304f0ab4b49d6172482533,
title = "Testing probabilistic equivalence through reinforcement learning",
abstract = "We propose a new approach to verification of probabilistic processes for which the model may not be available. We use a technique from Reinforcement Learning to approximate how far apart two processes are by solving a Markov Decision Process. If two processes are equivalent, the algorithm will return zero, otherwise it will provide a number and a test that witness the non equivalence. We suggest a new family of equivalences, called K-moment, for which it is possible to do so. The weakest, 1-moment equivalence, is trace-equivalence. The others are weaker than bisimulation but stronger than trace-equivalence.",
author = "Jos{\'e}e Desharnais and Fran{\c c}ois Laviolette and Sami Zhioua",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.; 26th International Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2006 ; Conference date: 13-12-2006 Through 15-12-2006",
year = "2006",
month = jan,
day = "1",
doi = "10.1007/11944836\_23",
language = "English",
isbn = "9783540499947",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "236--247",
editor = "Arun-Kumar, \{[initials] N.\}",
booktitle = "FSTTCS 2006",
}