The complexity of Markov decision processes
Mathematics of Operations Research
The Complexity of Decentralized Control of Markov Decision Processes
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
The complexity of multiagent systems: the price of silence
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Algorithms for sequential decision-making
Algorithms for sequential decision-making
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Solving decentralized POMDP problems using genetic algorithms
Autonomous Agents and Multi-Agent Systems
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In this paper, we study a particular subclass of partially observable models, called quasi-deterministic partially observable Markov decision processes (qDET-POMDPs), characterized by deterministic transitions and stochastic observations. While this framework does not model the same general problems as POMDPs, it still captures a number of interesting and challenging problems and have, in some cases, interesting properties. By studying the observability available in this subclass, we suggest that qDET-POMDPs may fall many steps in the complexity hierarchy. An extension of this framework to the decentralized case also reveals a subclass of numerous problems that can be approximated in polynomial space.