46 search results - page 5 / 10 » A Sparse Sampling Algorithm for Near-Optimal Planning in Lar... |
179
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AI
2011
Springer
14 years 7 months ago
2011
Springer
In this work, we explore how local interactions can simplify the process of decision-making in multiagent systems, particularly in multirobot problems. We review a recent decision-...
160
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JAIR
2008
15 years 4 months ago
2008
Probabilistic planning problems are typically modeled as a Markov Decision Process (MDP). MDPs, while an otherwise expressive model, allow only for sequential, non-durative action...
146
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JAIR
2006
15 years 4 months ago
2006
The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact s...
138
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IJCAI
2007
15 years 5 months ago
2007
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...
132
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ICTAI
2005
IEEE
15 years 9 months ago
2005
IEEE
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...