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37
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 448 (52 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.
Improved heterogeneous distance functions
 Journal of Artificial Intelligence Research
, 1997
"... Instancebased learning techniques typically handle continuous and linear input values well, but often do not handle nominal input attributes appropriately. The Value Difference Metric (VDM) was designed to find reasonable distance values between nominal attribute values, but it largely ignores cont ..."
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Cited by 199 (10 self)
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Instancebased learning techniques typically handle continuous and linear input values well, but often do not handle nominal input attributes appropriately. The Value Difference Metric (VDM) was designed to find reasonable distance values between nominal attribute values, but it largely ignores continuous attributes, requiring discretization to map continuous values into nominal values. This paper proposes three new heterogeneous distance functions, called the Heterogeneous Value Difference Metric (HVDM), the Interpolated Value Difference Metric (IVDM), and the Windowed Value Difference Metric (WVDM). These new distance functions are designed to handle applications with nominal attributes, continuous attributes, or both. In experiments on 48 applications the new distance metrics achieve higher classification accuracy on average than three previous distance functions on those datasets that have both nominal and continuous attributes.
Constructive Incremental Learning from Only Local Information
, 1998
"... ... This article illustrates the potential learning capabilities of purely local learning and offers an interesting and powerful approach to learning with receptive fields. ..."
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Cited by 160 (37 self)
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... This article illustrates the potential learning capabilities of purely local learning and offers an interesting and powerful approach to learning with receptive fields.
Locally Weighted Learning for Control
, 1996
"... Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We ex ..."
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Cited by 159 (17 self)
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Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how this affects the choice of learning paradigm. The discussion section explores the interesting impact that explicitly remembering all previous experiences has on the problem of learning to control.
Reinforcement Learning in the MultiRobot Domain
 Autonomous Robots
, 1997
"... This paper describes a formulation of reinforcement learning that enables learning in noisy, dynamic environemnts such as in the complex concurrent multirobot learning domain. The methodology involves minimizing the learning space through the use behaviors and conditions, and dealing with the credi ..."
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Cited by 135 (20 self)
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This paper describes a formulation of reinforcement learning that enables learning in noisy, dynamic environemnts such as in the complex concurrent multirobot learning domain. The methodology involves minimizing the learning space through the use behaviors and conditions, and dealing with the credit assignment problem through shaped reinforcement in the form of heterogeneous reinforcement functions and progress estimators. We experimentally validate the approach on a group of four mobile robots learning a foraging task. 1 Introduction Developing effective methods for realtime learning has been an ongoing challenge in autonomous agent research and is being explored in the mobile robot domain. In the last decade, reinforcement learning (RL), a class of approaches in which the agent learns based on reward and punishment it receives from the environment, has become the methodology of choice for learning in a variety of domains, including robotics. In this paper we describe a formulat...
A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... Many lazy learning algorithms are derivatives of the knearest neighbor (kNN) classifier, which uses a distance function to generate predictions from stored instances. Several studies have shown that kNN's performance is highly sensitive to the definition of its distance function. Many kNN v ..."
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Cited by 111 (0 self)
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Many lazy learning algorithms are derivatives of the knearest neighbor (kNN) classifier, which uses a distance function to generate predictions from stored instances. Several studies have shown that kNN's performance is highly sensitive to the definition of its distance function. Many kNN variants have been proposed to reduce this sensitivity by parameterizing the distance function with feature weights. However, these variants have not been categorized nor empirically compared. This paper reviews a class of weightsetting methods for lazy learning algorithms. We introduce a framework for distinguishing these methods and empirically compare them. We observed four trends from our experiments and conducted further studies to highlight them. Our results suggest that methods which use performance feedback to assign weight settings demonstrated three advantages over other methods: they require less preprocessing, perform better in the presence of interacting features, and generally require less training data to learn good settings. We also found that continuous weighting methods tend to outperform feature selection algorithms for tasks where some features are useful but less important than others.
Efficient Memorybased Learning for Robot Control
, 1990
"... This dissertation is about the application of machine learning to robot control. A system which has no initial model of the robot/world dynamics should be able to construct such a model using data received through its sensorsan approach which is formalized here as the $AB (StateActionBehaviour) ..."
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Cited by 108 (2 self)
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This dissertation is about the application of machine learning to robot control. A system which has no initial model of the robot/world dynamics should be able to construct such a model using data received through its sensorsan approach which is formalized here as the $AB (StateActionBehaviour) control cycle. A method of learning is presented in which all the experiences in the lifetime of the robot are explicitly remembered. The experiences are stored in a manner which permits fast recall of the closest previous experience to any new situation, thus permitting very quick predictions of the effects of proposed actions and, given a goal behaviour, permitting fast generation of a candidate action. The learning can take place in highdimensional nonlinear control spaces with realvalued ranges of variables. Furthermore, the method avoids a number of shortcomings of earlier learning methods in which the controller can become trapped in inadequate performance which does not improve. Also considered is how the system is made resistant to noisy inputs and how it adapts to environmental changes. A well founded mechanism for choosing actions is introduced which solves the experiment/perform dilemma for this domain with adequate computational efficiency, and with fast convergence to the goal behaviour. The dissertation explefins in detail how the $AB control cycle can be integrated into both low and high complexity tasks. The methods and algorithms are evaluated with numerous experiments using both real and simulated robot domefins. The final experiment also illustrates how a compound learning task can be structured into a hierarchy of simple learning tasks.
Explanationbased learning and reinforcement learning: A unified view
 In Proceedings Twelfth International Conference on Machs’ne Learning
, 1995
"... Abstract. In speeduplearning problems, where full descriptions of operators are known, both explanationbased learning (EBL) and reinforcement learning (RL) methods can be applied. This paper shows that both methods involve fundamentally the same process of propagating information backward from the ..."
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Cited by 46 (2 self)
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Abstract. In speeduplearning problems, where full descriptions of operators are known, both explanationbased learning (EBL) and reinforcement learning (RL) methods can be applied. This paper shows that both methods involve fundamentally the same process of propagating information backward from the goal toward the starting state. Most RL methods perform this propagation on a statebystate basis, while EBL methods compute the weakest preconditions of operators, and hence, perform this propagation on a regionbyregion basis. Barto, Bradtke, and Singh (1995) have observed that many algorithms for reinforcement learning can be viewed as asynchronous dynamic programming. Based on this observation, this paper shows how to develop dynamic programming versions of EBL, which we call regionbased dynamic programming or ExplanationBased Reinforcement Learning (EBRL). The paper compares batch and online versions of EBRL to batch and online versions of pointbased dynamic programming and to standard EBL. The results show that regionbased dynamic programming combines the strengths of EBL (fast learning and the ability to scale to large state spaces) with the strengths of reinforcement learning algorithms (learning of optimal policies). Results are shown in chess endgames and in synthetic maze tasks.
Memorybased Stochastic Optimization
 Neural Information Processing Systems 8
, 1995
"... In this paper we introduce new algorithms for optimizing noisy plants in which each experiment is very expensive. The algorithms build a global nonlinear model of the expected output at the same time as using Bayesian linear regression analysis of locally weighted polynomial models. The local model ..."
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Cited by 40 (7 self)
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In this paper we introduce new algorithms for optimizing noisy plants in which each experiment is very expensive. The algorithms build a global nonlinear model of the expected output at the same time as using Bayesian linear regression analysis of locally weighted polynomial models. The local model answers queries about confidence, noise, gradient and Hessians, and use them to make automated decisions similar to those made by a practitioner of Response Surface Methodology. The global and local models are combined naturally as a locally weighted regression. We examine the question of whether the global model can really help optimization, and we extend it to the case of timevarying functions. We compare the new algorithms with a highly tuned higherorder stochastic optimization algorithm on randomlygenerated functions and a simulated manufacturing task. We note significant improvements in total regret, time to converge, and final solution quality. 1 INTRODUCTION In a stochastic optim...
Computational aspects of motor control and motor learning
 Handbook of Perception and Action: Motor Skills
, 1996
"... 1 This chapter provides a basic introduction to various of the computational issues that arise in the study of motor control and motor learning. A broad set of topics is discussed, including feedback control, feedforward control, the problem of delay, observers, learning algorithms, motor learning, ..."
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Cited by 39 (2 self)
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1 This chapter provides a basic introduction to various of the computational issues that arise in the study of motor control and motor learning. A broad set of topics is discussed, including feedback control, feedforward control, the problem of delay, observers, learning algorithms, motor learning, and reference models. The goal of the chapter is to provide a unified discussion of these topics, emphasizing the complementary roles that they play in complex control systems. The choice of topics is motivated by their relevance to problems in motor control and motor learning; however, the chapter is not intended to be a review of specific models. Rather we emphasize basic theoretical issues with broad applicability. Many of the ideas described here are developed more fully in standard textbooks in modern systems theory, particularly textbooks on discretetime systems (˚Aström & Wittenmark, 1984), adaptive signal processing (Widrow & Stearns, 1985), and adaptive control systems (Goodwin & Sin, 1984; ˚Aström & Wittenmark, 1989). These texts assume a substantial background in control