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» Structure in the Space of Value Functions
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ICCBR
2005
Springer
13 years 11 months ago
CBR for State Value Function Approximation in Reinforcement Learning
CBR is one of the techniques that can be applied to the task of approximating a function over high-dimensional, continuous spaces. In Reinforcement Learning systems a learning agen...
Thomas Gabel, Martin A. Riedmiller
ISMVL
1997
IEEE
134views Hardware» more  ISMVL 1997»
13 years 10 months ago
Functional Decomposition of MVL Functions Using Multi-Valued Decision Diagrams
In this paper, the minimization of incompletely specified multi-valued functions using functional decomposition is discussed. From the aspect of machine learning, learning sample...
Craig M. Files, Rolf Drechsler, Marek A. Perkowski
IM
2008
13 years 6 months ago
The Structure of Geographical Threshold Graphs
We analyze the structure of random graphs generated by the geographical threshold model. The model is a generalization of random geometric graphs. Nodes are distributed in space, a...
Milan Bradonjic, Aric A. Hagberg, Allon G. Percus
JAIR
2008
171views more  JAIR 2008»
13 years 6 months ago
AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Graphical Models
Inspired by AND/OR search spaces for graphical models recently introduced, we propose to augment Multi-Valued Decision Diagrams (MDD) with AND nodes, in order to capture function ...
Robert Mateescu, Rina Dechter, Radu Marinescu 0002
QUESTA
1998
124views more  QUESTA 1998»
13 years 6 months ago
Structural results for the control of queueing systems using event-based dynamic programming
In this paper we study monotonicity results for optimal policies of various queueing and resource sharing models. The standard approach is to propagate, for each specific model, ...
Ger Koole