When Equivalence and Bisimulation Join Forces in Probabilistic Automata

TL;DR

This paper introduces a new distribution-based bisimulation for probabilistic automata that unifies existing equivalence and bisimilarity notions, enabling a more comprehensive analysis of automata behavior.

Contribution

It proposes a novel bisimulation concept that joins equivalence and bisimilarity, providing a unified framework for probabilistic automata analysis.

Findings

The new bisimulation bridges equivalence and bisimilarity concepts.

It enables the development of distribution-based bisimulation metrics.

The approach offers a robust notion of automata equivalence.

Abstract

Probabilistic automata were introduced by Rabin in 1963 as language acceptors. Two automata are equivalent if and only if they accept each word with the same probability. On the other side, in the process algebra community, probabilistic automata were re-proposed by Segala in 1995 which are more general than Rabin's automata. Bisimulations have been proposed for Segala's automata to characterize the equivalence between them. So far the two notions of equivalences and their characteristics have been studied most independently. In this paper, we consider Segala's automata, and propose a novel notion of distribution based bisimulation by joining the existing equivalence and bisimilarities. Our bisimulation bridges the two closely related concepts in the community, and provides a uniform way of studying their characteristics. We demonstrate the utility of our definition by studying distribution based bisimulation metrics, which gives rise to a robust notion of equivalence for Rabin's automata.