The complexity of Markov decision processes
Mathematics of Operations Research
Using abstractions for decision-theoretic planning with time constraints
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Xavier: a robot navigation architecture based on partially observable Markov decision process models
Artificial intelligence and mobile robots
Multi-time models for temporally abstract planning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A New Approach to Design Fuzzy Controllers for Mobile Robots Navigation
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Acting Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation
Acting Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation
Decomposition techniques for planning in stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Planning with deadlines in stochastic domains
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Hierarchical solution of Markov decision processes using macro-actions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Flexible decomposition algorithms for weakly coupled Markov decision problems
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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Markov Decision Processes have been successfully used in robotics for indoor robot navigation problems. They allow to compute optimal sequences of actions in order to achieve a given goal, accounting for actuators uncertainties. But MDPs are weak to avoid unknown obstacles. At the opposite reactive navigators are particularly adapted to that, and don't need any prior knowledge about the environment. But they are unable to plan the set of actions that will permit the realization of a given mission. We present a new state aggregation technique for Markov Decision Processes, such that part of the work usually dedicated to the planner is achieved by a reactive navigator. Thus some characteristics of our environments, such as width of corridors, have not to be considered, which allows to cluster states together, significantly reducing the state space. As a consequence, policies are computed faster and are shown to be at least as efficient as optimal ones.