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SARA
2005
Springer
14 years 2 months ago
Feature-Discovering Approximate Value Iteration Methods
Sets of features in Markov decision processes can play a critical role ximately representing value and in abstracting the state space. Selection of features is crucial to the succe...
Jia-Hong Wu, Robert Givan
ECAI
2004
Springer
14 years 2 months ago
Learning Complex and Sparse Events in Long Sequences
The Hierarchical Hidden Markov Model (HHMM) is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. Even if effective algorithms are ava...
Marco Botta, Ugo Galassi, Attilio Giordana
CDC
2009
IEEE
132views Control Systems» more  CDC 2009»
14 years 1 months ago
Q-learning and Pontryagin's Minimum Principle
Abstract— Q-learning is a technique used to compute an optimal policy for a controlled Markov chain based on observations of the system controlled using a non-optimal policy. It ...
Prashant G. Mehta, Sean P. Meyn
WOWMOM
2000
ACM
96views Multimedia» more  WOWMOM 2000»
14 years 1 months ago
An integrated mobility and traffic model for resource allocation in wireless networks
In a wireless communications network, the movement of mobile users presents significant technical challenges to providing efficient access to the wired broadband network. In this ...
Hisashi Kobayashi, Shun-Zheng Yu, Brian L. Mark
QOSA
2010
Springer
14 years 15 days ago
Parameterized Reliability Prediction for Component-Based Software Architectures
Critical properties of software systems, such as reliability, should be considered early in the development, when they can govern crucial architectural design decisions. A number o...
Franz Brosch, Heiko Koziolek, Barbora Buhnova, Ral...