85 search results - page 5 / 17 » Solving Stochastic Planning Problems with Large State and Ac... |
RSS
2007
13 years 9 months ago
2007
— We present a new motion planning framework that explicitly considers uncertainty in robot motion to maximize the probability of avoiding collisions and successfully reaching a ...
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...
ML
2002
ACM
13 years 7 months ago
2002
ACM
An issue that is critical for the application of Markov decision processes MDPs to realistic problems is how the complexity of planning scales with the size of the MDP. In stochas...
AAAI
2000
13 years 8 months ago
2000
Graphplan and heuristic state space planners such as HSP-R and UNPOP are currently two of the most effective approaches for solving classical planning problems. These approaches h...
ATAL
2007
Springer
14 years 1 months ago
2007
Springer
This paper contributes to solve effectively stochastic resource allocation problems known to be NP-Complete. To address this complex resource management problem, a Qdecomposition...