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187
Reinforcement Learning I: Introduction
, 1998
"... In which we try to give a basic intuitive sense of what reinforcement learning is and how it differs and relates to other fields, e.g., supervised learning and neural networks, genetic algorithms and artificial life, control theory. Intuitively, RL is trial and error (variation and selection, search ..."
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Cited by 3760 (98 self)
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In which we try to give a basic intuitive sense of what reinforcement learning is and how it differs and relates to other fields, e.g., supervised learning and neural networks, genetic algorithms and artificial life, control theory. Intuitively, RL is trial and error (variation and selection, search) plus learning (association, memory). We argue that RL is the only field that seriously addresses the special features of the problem of learning from interaction to achieve longterm goals.
The Extraction of Refined Rules from KnowledgeBased Neural Networks
 Machine Learning
, 1993
"... Neural networks, despite their empiricallyproven abilities, have been little used for the refinement of existing knowledge because this task requires a threestep process. First, knowledge in some form must be inserted into a neural network. Second, the network must be refined. Third, knowledge mus ..."
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Cited by 198 (4 self)
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Neural networks, despite their empiricallyproven abilities, have been little used for the refinement of existing knowledge because this task requires a threestep process. First, knowledge in some form must be inserted into a neural network. Second, the network must be refined. Third, knowledge must be extracted from the network. We have previously described a method for the first step of this process. Standard neural learning techniques can accomplish the second step. In this paper, we propose and empirically evaluate a method for the final, and possibly most difficult, step. This method efficiently extracts symbolic rules from trained neural networks. The four major results of empirical tests of this method are that the extracted rules: (1) closely reproduce (and can even exceed) the accuracy of the network from which they are extracted; (2) are superior to the rules produced by methods that directly refine symbolic rules; (3) are superior to those produced by previous techniques fo...
Learning and Sequential Decision Making
 LEARNING AND COMPUTATIONAL NEUROSCIENCE
, 1989
"... In this report we show how the class of adaptive prediction methods that Sutton called "temporal difference," or TD, methods are related to the theory of squential decision making. TD methods have been used as "adaptive critics" in connectionist learning systems, and have been proposed as models of ..."
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Cited by 195 (10 self)
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In this report we show how the class of adaptive prediction methods that Sutton called "temporal difference," or TD, methods are related to the theory of squential decision making. TD methods have been used as "adaptive critics" in connectionist learning systems, and have been proposed as models of animal learning in classical conditioning experiments. Here we relate TD methods to decision tasks formulated in terms of a stochastic dynamical system whose behavior unfolds over time under the influence of a decision maker's actions. Strategies are sought for selecting actions so as to maximize a measure of longterm payoff gain. Mathematically, tasks such as this can be formulated as Markovian decision problems, and numerous methods have been proposed for learning how to solve such problems. We show how a TD method can be understood as a novel synthesis of concepts from the theory of stochastic dynamic programming, which comprises the standard method for solving such tasks when a model of the dynamical system is available, and the theory of parameter estimation, which provides the appropriate context for studying learning rules in the form of equations for updating associative strengths in behavioral models, or connection weights in connectionist networks. Because this report is oriented primarily toward the nonengineer interested in animal learning, it presents tutorials on stochastic sequential decision tasks, stochastic dynamic programming, and parameter estimation.
A Theory of Cerebellar Function
, 1971
"... A comprehensive theory of cerebellar function is presented, which ties together the known anatomy and physiology of the cerebellum into a patternrecognition data processing system. The cerebellum is postulated to be functionally and structurally equivalent to a modification of the classical Percept ..."
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Cited by 178 (5 self)
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A comprehensive theory of cerebellar function is presented, which ties together the known anatomy and physiology of the cerebellum into a patternrecognition data processing system. The cerebellum is postulated to be functionally and structurally equivalent to a modification of the classical Perceptron patternclassification device. It is suggested that the mossy fiber  granule cell  Golgi cell input network performs an expansion recoding that enhances the patterndiscrimination capacity and learning speed of the cerebellar Purkinje response cells.
Neurofuzzy modeling and control
 IEEE Proceedings
, 1995
"... Abstract  Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framew ..."
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Cited by 147 (1 self)
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Abstract  Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called ANFIS (AdaptiveNetworkbased Fuzzy Inference System), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neurofuzzy approaches are also addressed. KeywordsFuzzy logic, neural networks, fuzzy modeling, neurofuzzy modeling, neurofuzzy control, ANFIS. I.
Kernel Methods for Relation Extraction
, 2002
"... We present an application of kernel methods to extracting relations from unstructured natural language sources. ..."
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Cited by 146 (0 self)
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We present an application of kernel methods to extracting relations from unstructured natural language sources.
KnowledgeBased Artificial Neural Networks
, 1994
"... Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for accurately classifying examples not seen during training. The challenge of hybrid learning systems is to use the information provided by one source of information to offset informat ..."
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Cited by 145 (13 self)
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Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for accurately classifying examples not seen during training. The challenge of hybrid learning systems is to use the information provided by one source of information to offset information missing from the other source. By so doing, a hybrid learning system should learn more effectively than systems that use only one of the information sources. KBANN(KnowledgeBased Artificial Neural Networks) is a hybrid learning system built on top of connectionist learning techniques. It maps problemspecific "domain theories", represented in propositional logic, into neural networks and then refines this reformulated knowledge using backpropagation. KBANN is evaluated by extensive empirical tests on two problems from molecular biology. Among other results, these tests show that the networks created by KBANN generalize better than a wide variety of learning systems, as well as several t...
Dynamic Branch Prediction with Perceptrons
"... This paper presents a new method for branch prediction. The key idea is to use one of the simplest possible neural networks, the perceptron, as an alternative to the commonly used twobit counters. Our predictor achieves increased accuracy by making use of long branch histories, which are possible b ..."
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Cited by 137 (18 self)
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This paper presents a new method for branch prediction. The key idea is to use one of the simplest possible neural networks, the perceptron, as an alternative to the commonly used twobit counters. Our predictor achieves increased accuracy by making use of long branch histories, which are possible because the hardware resources for our method scale linearly with the history length. By contrast, other purely dynamic schemes require exponential resources. We describe our design and evaluate it with respect to two well known predictors. We show that for a 4K byte hardware budget our method improves misprediction rates for the SPEC 2000 benchmarks by 10.1 % over the gshare predictor. Our experiments also provide a better understanding of the situations in which traditional predictors do and do not perform well. Finally, we describe techniques that allow our complex predictor to operate in one cycle.
On the Generalization Ability of Online Learning Algorithms
 IEEE Transactions on Information Theory
, 2001
"... In this paper we show that online algorithms for classification and regression can be naturally used to obtain hypotheses with good datadependent tail bounds on their risk. Our results are proven without requiring complicated concentrationofmeasure arguments and they hold for arbitrary onlin ..."
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Cited by 133 (8 self)
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In this paper we show that online algorithms for classification and regression can be naturally used to obtain hypotheses with good datadependent tail bounds on their risk. Our results are proven without requiring complicated concentrationofmeasure arguments and they hold for arbitrary online learning algorithms. Furthermore, when applied to concrete online algorithms, our results yield tail bounds that in many cases are comparable or better than the best known bounds.
On Photometric Issues in 3D Visual Recognition From A Single 2D Image
 International Journal of Computer Vision
, 1997
"... . We describe the problem of recognition under changing illumination conditions and changing viewing positions from a computational and human vision perspective. On the computational side we focus on the mathematical problems of creating an equivalence class for images of the same 3D object undergo ..."
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Cited by 108 (6 self)
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. We describe the problem of recognition under changing illumination conditions and changing viewing positions from a computational and human vision perspective. On the computational side we focus on the mathematical problems of creating an equivalence class for images of the same 3D object undergoing certain groups of transformations  mostly those due to changing illumination, and briefly discuss those due to changing viewing positions. The computational treatment culminates in proposing a simple scheme for recognizing, via alignment, an image of a familiar object taken from a novel viewing position and a novel illumination condition. On the human vision aspect, the paper is motivated by empirical evidence inspired by Mooney images of faces that suggest a relatively high level of visual processing is involved in compensating for photometric sources of variability, and furthermore, that certain limitations on the admissible representations of image information may exist. The psycho...