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Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
 Journal of Artificial Intelligence Research
, 2000
"... This paper presents a new approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and decomposing the value function of the target MDP into an additive combination of the value functions of the smaller MDPs. Th ..."
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Cited by 443 (6 self)
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. The decomposition, known as the MAXQ decomposition, has both a procedural semanticsas a subroutine hierarchyand a declarative semanticsas a representation of the value function of a hierarchical policy. MAXQ unifies and extends previous work on hierarchical reinforcement learning by Singh, Kaelbling
Functional Decomposition
, 1996
"... This paper presents a polynomial time algorithm for determining when a univariate rational function is the composition of two rational functions, and, if so, finds those rational functions. This algorithm is valid for rational functions over arbitrary fields and thus also resolves an outstanding pro ..."
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Cited by 28 (0 self)
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problem: polynomial decomposition over fields of finite characteristic. In addition, we present a framework that unifies the previous work on polynomial and rational function decomposition and provides some guidance for future work. Contents 1 Introduction 2 2 Applications 3 3 A General Framework 4 3
Functional Decomposition.
"... This paper shows a method to decompose a given multipleoutput circuit into two circuits with intermediate outputs. We use a BDD for characteristic function (BDD for CF) to represent a multipleoutput function. Many benchmark functions were realized by LUT cascades with intermediate outputs. Especial ..."
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This paper shows a method to decompose a given multipleoutput circuit into two circuits with intermediate outputs. We use a BDD for characteristic function (BDD for CF) to represent a multipleoutput function. Many benchmark functions were realized by LUT cascades with intermediate outputs
Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition
 in Conference Record of The TwentySeventh Asilomar Conference on Signals, Systems and Computers
, 1993
"... In this paper we describe a recursive algorithm to compute representations of functions with respect to nonorthogonal and possibly overcomplete dictionaries of elementary building blocks e.g. aiEne (wa.velet) frames. We propoeea modification to the Matching Pursuit algorithm of Mallat and Zhang (199 ..."
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Cited by 637 (1 self)
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In this paper we describe a recursive algorithm to compute representations of functions with respect to nonorthogonal and possibly overcomplete dictionaries of elementary building blocks e.g. aiEne (wa.velet) frames. We propoeea modification to the Matching Pursuit algorithm of Mallat and Zhang
A theory for multiresolution signal decomposition : the wavelet representation
 IEEE Transaction on Pattern Analysis and Machine Intelligence
, 1989
"... AbstractMultiresolution representations are very effective for analyzing the information content of images. We study the properties of the operator which approximates a signal at a given resolution. We show that the difference of information between the approximation of a signal at the resolutions ..."
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Cited by 3538 (12 self)
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2 ’ + ’ and 2jcan be extracted by decomposing this signal on a wavelet orthonormal basis of L*(R”). In LL(R), a wavelet orthonormal basis is a family of functions ( @ w (2’ ~n)),,,“jEZt, which is built by dilating and translating a unique function t+r (xl. This decomposition defines an orthogonal
Feature Transformation By Function Decomposition
, 1997
"... This article presents an approach to feature transfor1 mation that is based on the extension of the function decomposition method by Zupan et al. [5]. This method allows the decomposition to deal with functions that involve nominal (i.e., not necessarily binary) features, and is implemented in a sys ..."
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This article presents an approach to feature transfor1 mation that is based on the extension of the function decomposition method by Zupan et al. [5]. This method allows the decomposition to deal with functions that involve nominal (i.e., not necessarily binary) features, and is implemented in a
Machine Learning by Function Decomposition
, 1997
"... We present a new machine learning method that, given a set of training examples, induces a definition of the target concept in terms of a hierarchy of intermediate concepts and their definitions. This effectively decomposes the problem into smaller, less complex problems. The method is inspire ..."
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Cited by 21 (6 self)
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is inspired by the Boolean function decomposition approach to the design of digital circuits. To cope with high time complexity of finding an optimal decomposition, we propose a suboptimal heuristic algorithm. The method, implemented in program HINT (HIerarchy Induction Tool), is experimentally
Statecharts: A Visual Formalism For Complex Systems
, 1987
"... We present a broad extension of the conventional formalism of state machines and state diagrams, that is relevant to the specification and design of complex discreteevent systems, such as multicomputer realtime systems, communication protocols and digital control units. Our diagrams, which we cal ..."
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Cited by 2704 (56 self)
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alone behavioral description or as part of a more general design methodology that deals also with the system's other aspects, such as functional decomposition and dataflow specification. We also discuss some practical experience that was gained over the last three years in applying the statechart formalism
Algorithms for the Functional Decomposition of Laurent Polynomials
"... Abstract. Recent work has detailed the conditions under which univariate Laurent polynomials have functional decompositions. This paper presents algorithms to compute such univariate Laurent polynomial decompositions efficiently and gives their multivariate generalization. One application of functio ..."
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Cited by 1 (0 self)
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Abstract. Recent work has detailed the conditions under which univariate Laurent polynomials have functional decompositions. This paper presents algorithms to compute such univariate Laurent polynomial decompositions efficiently and gives their multivariate generalization. One application
Functional Decomposition of Symbolic Polynomials
"... Earlier work has presented algorithms to factor and compute GCDs of symbolic Laurent polynomials, that is multivariate polynomials whose exponents are themselves integervalued polynomials. This article extends the notion of univariate polynomial decomposition to symbolic polynomials and presents an ..."
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Cited by 8 (3 self)
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Earlier work has presented algorithms to factor and compute GCDs of symbolic Laurent polynomials, that is multivariate polynomials whose exponents are themselves integervalued polynomials. This article extends the notion of univariate polynomial decomposition to symbolic polynomials and presents
Results 1  10
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