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arXiv · 1706.06875

Multi-objective Robust Strategy Synthesis for Interval Markov Decision Processes

Ernst Moritz Hahn·Vahid Hashemi·Holger Hermanns·Morteza Lahijanian·Andrea Turrini
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Abstract

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.

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Ernst Moritz Hahn, Vahid Hashemi, Holger Hermanns, Morteza Lahijanian, Andrea Turrini. 2017-06-21. Multi-objective Robust Strategy Synthesis for Interval Markov Decision Processes. https://arxiv.org/abs/1706.06875

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