187 search results - page 15 / 38 » Hedging Uncertainty: Approximation Algorithms for Stochastic... |
APPROX
2005
Springer
15 years 3 months ago
2005
Springer
A large class of stochastic optimization problems can be modeled as minimizing an objective function f that depends on a choice of a vector x ∈ X, as well as on a random external...
UAI
1998
14 years 11 months ago
1998
This paper presents two new approaches to decomposing and solving large Markov decision problems (MDPs), a partial decoupling method and a complete decoupling method. In these app...
PODS
2008
ACM
15 years 9 months ago
2008
ACM
There is an increasing quantity of data with uncertainty arising from applications such as sensor network measurements, record linkage, and as output of mining algorithms. This un...
AAAI
2000
14 years 11 months ago
2000
Models for sequential decision making under uncertainty (e.g., Markov decision processes,or MDPs) have beenstudied in operations research for decades. The recent incorporation of ...
ATAL
2008
Springer
14 years 11 months ago
2008
Springer
The problem of coalition formation when agents are uncertain about the types or capabilities of their potential partners is a critical one. In [3] a Bayesian reinforcement learnin...