Bisimulation through probabilistic testing
Information and Computation
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Metrics for labelled Markov processes
Theoretical Computer Science - Logic, semantics and theory of programming
Metrics for finite Markov decision processes
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Approximating and computing behavioural distances in probabilistic transition systems
Theoretical Computer Science
On the complexity of computing probabilistic bisimilarity
FOSSACS'12 Proceedings of the 15th international conference on Foundations of Software Science and Computational Structures
On-the-Fly exact computation of bisimilarity distances
TACAS'13 Proceedings of the 19th international conference on Tools and Algorithms for the Construction and Analysis of Systems
| Hi-index | 0.00 |
This paper presents a library for exactly computing the bisimilarity Kantorovich-based pseudometrics between Markov chains and between Markov decision processes. These are distances that measure the behavioral discrepancies between non-bisimilar systems. They are computed by using an on-the-fly greedy strategy that prevents the exhaustive state space exploration and does not require a complete storage of the data structures. Tests performed on a consistent set of (pseudo)randomly generated instances show that our algorithm improves the efficiency of the previously proposed iterative algorithms, on average, with orders of magnitude. The tool is available as a Mathematica package library.