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AIPS
2008
13 years 9 months ago
Bounded-Parameter Partially Observable Markov Decision Processes
The POMDP is considered as a powerful model for planning under uncertainty. However, it is usually impractical to employ a POMDP with exact parameters to model precisely the real-...
Yaodong Ni, Zhi-Qiang Liu
ICWS
2004
IEEE
13 years 8 months ago
Dynamic Workflow Composition using Markov Decision Processes
The advent of Web services has made automated workflow composition relevant to Web based applications. One technique that has received some attention, for automatically composing ...
Prashant Doshi, Richard Goodwin, Rama Akkiraju, Ku...
TALG
2010
73views more  TALG 2010»
13 years 5 months ago
Discounted deterministic Markov decision processes and discounted all-pairs shortest paths
We present two new algorithms for finding optimal strategies for discounted, infinite-horizon, Deterministic Markov Decision Processes (DMDP). The first one is an adaptation of...
Omid Madani, Mikkel Thorup, Uri Zwick
ICML
2006
IEEE
14 years 8 months ago
Probabilistic inference for solving discrete and continuous state Markov Decision Processes
Inference in Markov Decision Processes has recently received interest as a means to infer goals of an observed action, policy recognition, and also as a tool to compute policies. ...
Marc Toussaint, Amos J. Storkey
LICS
2007
IEEE
14 years 1 months ago
Limits of Multi-Discounted Markov Decision Processes
Markov decision processes (MDPs) are controllable discrete event systems with stochastic transitions. The payoff received by the controller can be evaluated in different ways, dep...
Hugo Gimbert, Wieslaw Zielonka