Explanation-Based Neural Network Learning: A Lifelong Learning Approach / Edition 1

by Sebastian Thrun

Overview

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. Explanation-Based Neural Network Learning: A Lifelong Learning Approach 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.

Product Details

ISBN-13:	9780792397168
Publisher:	Springer US
Publication date:	04/30/1996
Series:	The Springer International Series in Engineering and Computer Science , #357
Edition description:	1996
Pages:	264
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

1 Introduction.- 1.1 Motivation.- 1.2 Lifelong Learning.- 1.3 A Simple Complexity Consideration.- 1.4 The EBNN Approach to Lifelong Learning.- 1.5 Overview.- 2 Explanation-Based Neural Network Learning.- 2.1 Inductive Neural Network Learning.- 2.2 Analytical Learning.- 2.3 Why Integrate Induction and Analysis?.- 2.4 The EBNN Learning Algorithm.- 2.5 A Simple Example.- 2.6 The Relation of Neural and Symbolic Explanation-Based Learning.- 2.7 Other Approaches that Combine Induction and Analysis.- 2.8 EBNN and Lifelong Learning.- 3 The Invariance Approach.- 3.1 Introduction.- 3.2 Lifelong Supervised Learning.- 3.3 The Invariance Approach.- 3.4 Example: Learning to Recognize Objects.- 3.5 Alternative Methods.- 3.6 Remarks.- 4 Reinforcement Learning.- 4.1 Learning Control.- 4.2 Lifelong Control Learning.- 4.3 Q-Learning.- 4.4 Generalizing Function Approximators and Q-Learning.- 4.5 Remarks.- 5 Empirical Results.- 5.1 Learning Robot Control.- 5.2 Navigation.- 5.3 Simulation.- 5.4 Approaching and Grasping a Cup.- 5.5 NeuroChess.- 5.6 Remarks.- 6 Discussion.- 6.1 Summary.- 6.2 Open Problems.- 6.3 Related Work.- 6.4 Concluding Remarks.- A An Algorithm for Approximating Values and Slopes with Artificial Neural Networks.- A.1 Definitions.- A.2 Network Forward Propagation.- A.3 Forward Propagation of Auxiliary Gradients.- A.4 Error Functions.- A.5 Minimizing the Value Error.- A.6 Minimizing the Slope Error.- A.7 The Squashing Function and its Derivatives.- A.8 Updating the Network Weights and Biases.- B Proofs of the Theorems.- C Example Chess Games.- C.1 Game 1.- C.2 Game 2.- References.- List of Symbols.

Editorial Reviews

Describes a paradigm for machine learning that may open a new generation of methods, especially for situations in which a series of different learning tasks provides an opportunity for synergy among them. The explanation-based neural network approach transfers knowledge across multiple learning tasks, allowing domain knowledge accumulated in previous learning efforts to guide generalization in new learning tasks. The result is more accurate generalizations with less data than previous methods. The method is demonstrated in contexts of supervised learning, reinforced learning, robotics, and chess. Annotation c. by Book News, Inc., Portland, Or.

Booknews