Robot Learning

Robot Learning

Paperback(Softcover reprint of the original 1st ed. 1993)

$149.99
Choose Expedited Shipping at checkout for guaranteed delivery by Thursday, April 15

Overview

Building a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans from much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action.
Although its roots can be traced back to the late fifties, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fueled by exciting new work in the areas of reinforcement earning, behavior-based architectures, genetic algorithms, neural networks and the study of artificial life. Robot Learning gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in this book include: reinforcement learning, behavior-based architectures, neural networks, map learning, action models, navigation and guided exploration.

Product Details

ISBN-13: 9781461363965
Publisher: Springer US
Publication date: 09/27/2012
Series: The Springer International Series in Engineering and Computer Science , #233
Edition description: Softcover reprint of the original 1st ed. 1993
Pages: 240
Product dimensions: 6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

Preface. 1. Introduction to Robot Learning; J.H. Connell, S. Mahadevan. 2. Knowledge-Based Training of Artificial Neural Networks for Autonomous Robot Driving; D.A. Pomerleau. 3. Learning Multiple Goal Behavior via Task Decomposition and Dynamic Policy Merging; S. Whitehead, J. Karlsson, J. Tenenberg. 4. Memory-Based Reinforcement Learnings: Converging with Less Data and Less Real Time; A.W. Moore, C.G. Atkeson. 5. Rapid Task Learning for Real Robots; J.H. Connell, S. Mahadevan. 6. The Semantic Hierarchy in Robot Learning; B. Kuipers, R. Froom, Wan-Yik Lee, D. Pierce. 7. Uncertainty in Graph-Based Map Learning; T. Dean, K. Basye, L. Kaelbling. 8. Real Robots, Real Learning Problems; R.A. Brooks, M.J. Mataric.. Bibliography. Index.

Customer Reviews