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ICTAI
2005
IEEE
13 years 11 months ago
Planning with POMDPs Using a Compact, Logic-Based Representation
Partially Observable Markov Decision Processes (POMDPs) provide a general framework for AI planning, but they lack the structure for representing real world planning problems in a...
Chenggang Wang, James G. Schmolze
AAAI
1996
13 years 6 months ago
Computing Optimal Policies for Partially Observable Decision Processes Using Compact Representations
: Partially-observable Markov decision processes provide a very general model for decision-theoretic planning problems, allowing the trade-offs between various courses of actions t...
Craig Boutilier, David Poole
AIPS
2007
13 years 7 months ago
Mixed Integer Linear Programming for Exact Finite-Horizon Planning in Decentralized Pomdps
We consider the problem of finding an n-agent jointpolicy for the optimal finite-horizon control of a decentralized Pomdp (Dec-Pomdp). This is a problem of very high complexity ...
Raghav Aras, Alain Dutech, François Charpil...
IJRR
2011
218views more  IJRR 2011»
13 years 6 days ago
Motion planning under uncertainty for robotic tasks with long time horizons
Abstract Partially observable Markov decision processes (POMDPs) are a principled mathematical framework for planning under uncertainty, a crucial capability for reliable operation...
Hanna Kurniawati, Yanzhu Du, David Hsu, Wee Sun Le...
IJRR
2010
162views more  IJRR 2010»
13 years 3 months ago
Planning under Uncertainty for Robotic Tasks with Mixed Observability
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been app...
Sylvie C. W. Ong, Shao Wei Png, David Hsu, Wee Sun...