Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6876196 | Theoretical Computer Science | 2014 | 17 Pages |
Abstract
We consider models of programs that incorporate probability, dense real-time and data. We present a new abstraction refinement method for computing minimum and maximum reachability probabilities for such models. Our approach uses strictly local refinement steps to reduce both the size of abstractions generated and the complexity of operations needed, in comparison to previous approaches of this kind. We implement the techniques and evaluate them on a selection of large case studies, including some infinite-state probabilistic real-time models, demonstrating improvements over existing tools in several cases.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Klaus Dräger, Marta Kwiatkowska, David Parker, Hongyang Qu,