Generation of Counterexamples for Model Checking of Markov Decision Processes

Abstract

The practical usefulness of a model checker as a debugging tool relies on its ability to provide diagnostic information, sometimes also referred to as a counterexample. Current stochastic model checkers do not provide such diagnostic information. In this paper we address this problem for Markov Decision Processes. First, we define the notion of counterexamples in this context. Then, we discuss three methods to generate informative counterexamples which are helpful in system debugging. We point out the advantages and drawbacks of each method. We also experimentally compare all three methods. Our experiments demonstrate the conditions under which each method is appropriate to be used.