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» Complexity of Planning with Partial Observability
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AAAI
2007
13 years 11 months ago
Purely Epistemic Markov Decision Processes
Planning under uncertainty involves two distinct sources of uncertainty: uncertainty about the effects of actions and uncertainty about the current state of the world. The most wi...
Régis Sabbadin, Jérôme Lang, N...
CDC
2009
IEEE
148views Control Systems» more  CDC 2009»
14 years 1 months ago
An adaptive artificial potential function approach for geometric sensing
In this paper, a novel artificial potential function is proposed for planning the path of a robotic sensor in a partially observed environment containing multiple obstacles and mul...
Guoxian Zhang, Silvia Ferrari
FLAIRS
2001
13 years 10 months 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
NIPS
2007
13 years 10 months ago
Competition Adds Complexity
It is known that determinining whether a DEC-POMDP, namely, a cooperative partially observable stochastic game (POSG), has a cooperative strategy with positive expected reward is ...
Judy Goldsmith, Martin Mundhenk
AAAI
2010
13 years 10 months ago
Probabilistic Plan Recognition Using Off-the-Shelf Classical Planners
Plan recognition is the problem of inferring the goals and plans of an agent after observing its behavior. Recently, it has been shown that this problem can be solved efficiently,...
Miquel Ramírez, Hector Geffner