Splitter Orderings for Probabilistic Bisimulation

TL;DR

This paper introduces new techniques to speed up probabilistic bisimulation computations, significantly reducing runtime for state space reduction in stochastic systems with nondeterministic behaviors.

Contribution

It proposes ordering heuristics and hash table methods to accelerate iterative bisimulation algorithms, improving efficiency over existing approaches.

Findings

Reduced running time by one order of magnitude on average

Effective heuristics for selecting splitter blocks

Hash tables decrease average complexity of the iterative process

Abstract

Model checking has been proposed as a formal verification approach for analyzing computer-based and cyber-physical systems. The state space explosion problem is the main obstacle for applying this approach for sophisticated systems. Bisimulation minimization is a prominent method for reducing the number of states in a labeled transition system and is used to alleviate the challenges of the state space explosion problem. For systems with stochastic behaviors, probabilistic bisimulation is used to reduce a given model to its minimized equivalent one. In recent years, several techniques have been proposed to reduce the time complexity of the iterative methods for computing probabilistic bisimulation of stochastic systems with nondeterministic behaviors. In this paper, we propose several techniques to accelerate iterative processes to partition the state space of a given probabilistic model to its bisimulation classes. The first technique applies two ordering heuristics for choosing splitter blocks. The second technique uses hash tables to reduce the running time and the average time complexity of the standard iterative method. The proposed approaches are implemented and run on several conventional case studies and reduce the running time by one order of magnitude on average.