Book announcement: EBNN, Lifelong Learning

[thrun+@heaven.learning.cs.cmu.edu]

I have the pleasure to announce the following book.

 

	EXPLANATION-BASED NEURAL NETWORK LEARNING:
	A Lifelong Learning Approach

	Sebastian Thrun

	Carnegie Mellon University & University of Bonn
	published by Kluwer Academic Publishers





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Lifelong learning addresses situations in which a learner faces a
series of different learning tasks, providing the opportunity for
synergy among them. Explanation-based neural network learning (EBNN) is
a machine learning algorithm that transfers knowledge across multiple
learning tasks. When faced with a new learning task, EBNN exploits
domain knowledge accumulated in previous learning tasks to guide
generalization in the new one. As a result, EBNN generalizes more
accurately from less data than comparable methods. This book describes
the basic EBNN paradigm and investigates it in the context of
supervised learning, reinforcement learning, robotics, and chess.

``The paradigm of lifelong learning - using earlier learned knowledge
to improve subsequent learning - is a promising direction for a new
generation of machine learning algorithms. Given the need for more
accurate learning methods, it is difficult to imagine a future for
machine learning that does not include this paradigm.'' -- from the
Foreword by Tom M. Mitchell

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   FOREWORD by Tom Mitchell                                              ix

   PREFACE                                                               xi

   1 INTRODUCTION                                                        1

      1.1 Motivation                                                     1
      1.2 Lifelong Learning                                              3
      1.3 A Simple Complexity Consideration                              8
      1.4 The EBNN Approach to Lifelong Learning                         13
      1.5 Overview                                                       16

   2 EXPLANATION-BASED NEURAL NETWORK LEARNING                           19

      2.1 Inductive Neural Network Learning                              20
      2.2 Analytical Learning                                            27
      2.3 Why Integrate Induction and Analysis?                          31
      2.4 The EBNN Learning Algorithm                                    33
      2.5 A Simple Example                                               39
      2.6 The Relation of Neural and Symbolic Explanation-Based Learning 43
      2.7 Other Approaches that Combine Induction and Analysis           45
      2.8 EBNN and Lifelong Learning                                     47

   3 THE INVARIANCE APPROACH                                             49

      3.1 Introduction                                                   49
      3.2 Lifelong Supervised Learning                                   50
      3.3 The Invariance Approach                                        55
      3.4 Example: Learning to Recognize Objects                         59
      3.5 Alternative Methods                                            74
      3.6 Remarks                                                        90

   4 REINFORCEMENT LEARNING                                              93

      4.1 Learning Control                                               94
      4.2 Lifelong Control Learning                                      98
      4.3 Q-Learning                                                     102
      4.4 Generalizing Function Approximators and Q-Learning             111
      4.5 Remarks                                                        125

   5 EMPIRICAL RESULTS                                                   131

      5.1 Learning Robot Control                                         132
      5.2 Navigation                                                     133
      5.3 Simulation                                                     141
      5.4 Approaching and Grasping a Cup                                 146
      5.5 NeuroChess                                                     152
      5.6 Remarks                                                        175

   6 DISCUSSION                                                          177

      6.1 Summary                                                        177
      6.2 Open Problems                                                  181
      6.3 Related Work                                                   185
      6.4 Concluding Remarks                                             192

   A AN ALGORITHM FOR APPROXIMATING VALUES AND SLOPES WITH ARTIFICIAL
          NEURAL NETWORKS                                                195

      A.1 Definitions                                                    196
      A.2 Network Forward Propagation                                    196
      A.3 Forward Propagation of Auxiliary Gradients                     197
      A.4 Error Functions                                                198
      A.5 Minimizing the Value Error                                     199
      A.6 Minimizing the Slope Error                                     199
      A.7 The Squashing Function and its Derivatives                     201
      A.8 Updating the Network Weights and Biases                        202

   B PROOFS OF THE THEOREMS                                              203

   C EXAMPLE CHESS GAMES                                                 207

      C.1 Game 1                                                         207
      C.2 Game 2                                                         219

   REFERENCES                                                            227

   LIST OF SYMBOLS                                                       253

   INDEX                                                                 259


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More information concerning this book:
	http://www.cs.cmu.edu/~thrun/papers/thrun.book.html
	http://www.informatik.uni-bonn.de/~thrun/papers/thrun.book.html