84 search results - page 6 / 17 » A New Perspective on Algorithms for Optimizing Policies unde... |
CCE
2004
13 years 7 months ago
2004
The algorithmic framework developed for improving heuristic solutions of the new version of deterministic TSP [Choi et al., 2002] is extended to the stochastic case. To verify the...
PKDD
2010
Springer
13 years 5 months ago
2010
Springer
Partially observable Markov decision processes (POMDPs) are widely used for planning under uncertainty. In many applications, the huge size of the POMDP state space makes straightf...
IJCAI
2003
13 years 8 months ago
2003
Markov decision processes (MDPs) and contingency planning (CP) are two widely used approaches to planning under uncertainty. MDPs are attractive because the model is extremely gen...
DATE
2005
IEEE
14 years 1 months ago
2005
IEEE
Context-aware applications pose new challenges, including a need for new computational models, uncertainty management, and efficient optimization under uncertainty. Uncertainty c...
ATAL
2007
Springer
14 years 1 months ago
2007
Springer
Planning in single-agent models like MDPs and POMDPs can be carried out by resorting to Q-value functions: a (near-) optimal Q-value function is computed in a recursive manner by ...