Qualitative Controller Synthesis for Consumption Markov Decision Processes [chapter]

František Blahoudek, Tomáš Brázdil, Petr Novotný, Melkior Ornik, Pranay Thangeda, Ufuk Topcu
2020 Lecture Notes in Computer Science  
Consumption Markov Decision Processes (CMDPs) are probabilistic decision-making models of resource-constrained systems. In a CMDP, the controller possesses a certain amount of a critical resource, such as electric power. Each action of the controller can consume some amount of the resource. Resource replenishment is only possible in special reload states, in which the resource level can be reloaded up to the full capacity of the system. The task of the controller is to prevent resource
more » ... n, i.e. ensure that the available amount of the resource stays non-negative, while ensuring an additional linear-time property. We study the complexity of strategy synthesis in consumption MDPs with almost-sure Büchi objectives. We show that the problem can be solved in polynomial time. We implement our algorithm and show that it can efficiently solve CMDPs modelling real-world scenarios.
doi:10.1007/978-3-030-53291-8_22 fatcat:c32sewrmirhtvorq47f6g4xopy