46 search results - page 5 / 10 » A Sparse Sampling Algorithm for Near-Optimal Planning in Lar... |
AI
2011
Springer
12 years 11 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-...
JAIR
2008
13 years 7 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...
JAIR
2006
13 years 7 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...
IJCAI
2007
13 years 9 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...
ICTAI
2005
IEEE
14 years 1 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...