Multi-objective Robust Strategy Synthesis for Interval Markov Decision Processes

Ernst Moritz Hahn, Vahid Hashemi, Holger Hermanns, Morteza Lahijanian, Andrea Turrini

Interval Markov decision processes (IMDPs) generalise classical MDPs by having interval-valued transition probabilities. They provide a powerful modelling tool for probabilistic systems with an additional variation or uncertainty that prevents the knowledge of the exact transition probabilities. In this paper, we consider the problem of multi-objective robust strategy synthesis for interval MDPs, where the aim is to find a robust strategy that guarantees the satisfaction of multiple properties at the same time in face of the transition probability uncertainty. We first show that this problem is PSPACE-hard. Then, we provide a value iteration-based decision algorithm to approximate the Pareto set of achievable points. We finally demonstrate the practical effectiveness of our proposed approaches by applying them on several case studies using a prototypical tool.

Knowledge Graph

arrow_drop_up

Comments

Sign up or login to leave a comment