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IJCAI
2007
14 years 13 days ago
A Hybridized Planner for Stochastic Domains
Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabil...
Mausam, Piergiorgio Bertoli, Daniel S. Weld
FLAIRS
2001
14 years 11 days ago
Probabilistic Planning for Behavior-Based Robots
Partially Observable Markov Decision Process models (POMDPs) have been applied to low-level robot control. We show how to use POMDPs differently, namely for sensorplanning in the ...
Amin Atrash, Sven Koenig
AAAI
1994
14 years 9 days ago
Acting Optimally in Partially Observable Stochastic Domains
In this paper, we describe the partially observable Markov decision process pomdp approach to nding optimal or near-optimal control strategies for partially observable stochastic ...
Anthony R. Cassandra, Leslie Pack Kaelbling, Micha...
ICRA
2010
IEEE
101views Robotics» more  ICRA 2010»
13 years 9 months ago
Multirobot coordination by auctioning POMDPs
— We consider the problem of task assignment and execution in multirobot systems, by proposing a procedure for bid estimation in auction protocols. Auctions are of interest to mu...
Matthijs T. J. Spaan, Nelson Gonçalves, Jo&...
ICML
2006
IEEE
14 years 11 months ago
PAC model-free reinforcement learning
For a Markov Decision Process with finite state (size S) and action spaces (size A per state), we propose a new algorithm--Delayed Q-Learning. We prove it is PAC, achieving near o...
Alexander L. Strehl, Lihong Li, Eric Wiewiora, Joh...