Debugging of Markov Decision Processes (MDPs) Models

TL;DR

This paper introduces a diagnostic method for analyzing probabilistic counterexamples in Markov Decision Processes, aiding users in identifying the model components responsible for probability threshold violations.

Contribution

It proposes a causality-based approach to interpret counterexamples in probabilistic model checking of MDPs, enhancing debugging capabilities.

Findings

The method effectively highlights relevant model parts causing violations.

It improves understanding of counterexamples in probabilistic model checking.

The approach guides users to critical model components for debugging.

Abstract

In model checking, a counterexample is considered as a valuable tool for debugging. In Probabilistic Model Checking (PMC), counterexample generation has a quantitative aspect. The counterexample in PMC is a set of paths in which a path formula holds, and their accumulative probability mass violates the probability threshold. However, understanding the counterexample is not an easy task. In this paper we address the task of counterexample analysis for Markov Decision Processes (MDPs). We propose an aided-diagnostic method for probabilistic counterexamples based on the notions of causality, responsibility and blame. Given a counterexample for a Probabilistic CTL (PCTL) formula that does not hold over an MDP model, this method guides the user to the most relevant parts of the model that led to the violation.