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Explanation-Based Neural Network Learning A Lifelong Learning Approach

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...

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Bibliographic Details
Main Author: Thrun, Sebastian
Format: eBook
Language:English
Published: New York, NY Springer US 1996, 1996
Edition:1st ed. 1996
Series:The Springer International Series in Engineering and Computer Science
Subjects:
Complex Systems
Artificial Intelligence
System Theory
Mathematical Physics
Theoretical, Mathematical And Computational Physics
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
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

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