44 search results - page 3 / 9 » Approximate inference for planning in stochastic relational ... |
JAIR
2008
13 years 8 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...
NIPS
2003
13 years 10 months ago
2003
A mobile robot acting in the world is faced with a large amount of sensory data and uncertainty in its action outcomes. Indeed, almost all interesting sequential decision-making d...
NIPS
1998
13 years 10 months ago
1998
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
TROB
2010
13 years 7 months ago
2010
—Robotic systems need to be able to plan control actions that are robust to the inherent uncertainty in the real world. This uncertainty arises due to uncertain state estimation,...
NIPS
2003
13 years 10 months ago
2003
We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual...