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» How to Dynamically Merge Markov Decision Processes
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ALT
2007
Springer
15 years 6 months ago
Pseudometrics for State Aggregation in Average Reward Markov Decision Processes
We consider how state similarity in average reward Markov decision processes (MDPs) may be described by pseudometrics. Introducing the notion of adequate pseudometrics which are we...
Ronald Ortner
PUK
2000
14 years 11 months ago
Dynamic Scheduling of Progressive Processing Plans
Progressive processing plans allow systems to tradeoff computational resources against the quality of service by specifying alternative ways in which to accomplish each step. When ...
Shlomo Zilberstein, Abdel-Illah Mouaddib, Andrew A...
AAAI
1997
14 years 11 months ago
Incremental Methods for Computing Bounds in Partially Observable Markov Decision Processes
Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect ...
Milos Hauskrecht
ICML
2010
IEEE
14 years 10 months ago
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions re...
Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zi...
AI
2008
Springer
14 years 9 months ago
Reachability analysis of uncertain systems using bounded-parameter Markov decision processes
Verification of reachability properties for probabilistic systems is usually based on variants of Markov processes. Current methods assume an exact model of the dynamic behavior a...
Di Wu, Xenofon D. Koutsoukos