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IJCAI
2001
14 years 11 months ago
Complexity of Probabilistic Planning under Average Rewards
A general and expressive model of sequential decision making under uncertainty is provided by the Markov decision processes (MDPs) framework. Complex applications with very large ...
Jussi Rintanen
AROBOTS
2008
166views more  AROBOTS 2008»
14 years 8 months ago
User-adapted plan recognition and user-adapted shared control: A Bayesian approach to semi-autonomous wheelchair driving
Abstract Many elderly and physically impaired people experience difficulties when maneuvering a powered wheelchair. In order to provide improved maneuvering, powered wheelchairs ha...
Eric Demeester, Alexander Hüntemann, Dirk Van...
ATAL
2006
Springer
15 years 1 months ago
Winning back the CUP for distributed POMDPs: planning over continuous belief spaces
Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are evolving as a popular approach for modeling multiagent systems, and many different algorithms ha...
Pradeep Varakantham, Ranjit Nair, Milind Tambe, Ma...
85
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IROS
2006
IEEE
121views Robotics» more  IROS 2006»
15 years 3 months ago
Planning and Acting in Uncertain Environments using Probabilistic Inference
— An important problem in robotics is planning and selecting actions for goal-directed behavior in noisy uncertain environments. The problem is typically addressed within the fra...
Deepak Verma, Rajesh P. N. Rao
AIPS
2008
14 years 12 months ago
Multiagent Planning Under Uncertainty with Stochastic Communication Delays
We consider the problem of cooperative multiagent planning under uncertainty, formalized as a decentralized partially observable Markov decision process (Dec-POMDP). Unfortunately...
Matthijs T. J. Spaan, Frans A. Oliehoek, Nikos A. ...