Qualitative Logics and Equivalences for Probabilistic Systems
Krishnendu Chatterjee, Luca de Alfaro, Marco Faella, Axel Legay

TL;DR
This paper introduces QRCTL, a logic for qualitative analysis of Markov Decision Processes, along with algorithms to compute qualitative equivalences, and explores their relation to bisimulation in different types of MDPs.
Contribution
The paper defines QRCTL and QRCTL*, establishes their relation to existing bisimulation concepts, and provides polynomial-time algorithms for qualitative equivalence in finite alternating MDPs.
Findings
QRCTL captures properties with probabilities 0 or 1.
Qualitative equivalence coincides with alternating bisimulation in finite alternating MDPs.
Efficient algorithms exist for computing qualitative equivalence in certain classes of MDPs.
Abstract
We investigate logics and equivalence relations that capture the qualitative behavior of Markov Decision Processes (MDPs). We present Qualitative Randomized CTL (QRCTL): formulas of this logic can express the fact that certain temporal properties hold over all paths, or with probability 0 or 1, but they do not distinguish among intermediate probability values. We present a symbolic, polynomial time model-checking algorithm for QRCTL on MDPs. The logic QRCTL induces an equivalence relation over states of an MDP that we call qualitative equivalence: informally, two states are qualitatively equivalent if the sets of formulas that hold with probability 0 or 1 at the two states are the same. We show that for finite alternating MDPs, where nondeterministic and probabilistic choices occur in different states, qualitative equivalence coincides with alternating bisimulation, and can thus be…
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