Explanation Based Neural Network Learning

Author: Sebastian Thrun
Publisher: Springer Science & Business Media
ISBN: 9781461313816
Size: 15.88 MB
Format: PDF, ePub, Mobi
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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. 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.

Interdisciplinary Approaches To Robot Learning

Author: John Demiris
Publisher: World Scientific
ISBN: 9812792740
Size: 20.60 MB
Format: PDF, ePub, Mobi
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This collection of papers explore the variety of techniques used to equip robots with the capacity to improve their behaviour over time, based upon their incoming experiences. The contributions are interdisciplinary in nature and combine research from the field of robotics, computer science and biology.

Encyclopedia Of The Sciences Of Learning

Author: Norbert M. Seel
Publisher: Springer Science & Business Media
ISBN: 9781441914279
Size: 16.98 MB
Format: PDF, Mobi
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This publication reflects the myriad theories that have developed over the past century to explain how we acquire, organize and deploy knowledge and skills. Providing up-to-date, wide-ranging and authoritative coverage, this is the first reference of its kind.

Mind Matters

Author: Allen Newell
Publisher: Psychology Press
ISBN: 0805813640
Size: 19.52 MB
Format: PDF, Kindle
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Based on a symposium honoring the extensive work of Allen Newell -- one of the founders of artificial intelligence, cognitive science, human-computer interaction, and the systematic study of computational architectures -- this volume demonstrates how unifying themes may be found in the diversity that characterizes current research on computers and cognition. The subject matter includes: * an overview of cognitive and computer science by leading researchers in the field; * a comprehensive description of Allen Newell's "Soar" -- a computational architecture he developed as a unified theory of cognition; * commentary on how the Soar theory of cognition relates to important issues in cognitive and computer science; * rigorous treatments of controversial issues in cognition -- methodology of cognitive science, hybrid approaches to machine learning, word-sense disambiguation in understanding material language, and the role of capability processing constraints in architectural theory; * comprehensive and systematic methods for studying architectural evolution in both hardware and software; * a thorough discussion of the use of analytic models in human computer interaction; * extensive reviews of important experiments in the study of scientific discovery and deduction; and * an updated analysis of the role of symbols in information processing by Herbert Simon. Incorporating the research of top scientists inspired by Newell's work, this volume will be of strong interest to a large variety of scientific communities including psychologists, computational linguists, computer scientists and engineers, and interface designers. It will also be valuable to those who study the scientific process itself, as it chronicles the impact of Newell's approach to research, simultaneously delving into each scientific discipline and producing results that transcend the boundaries of those disciplines.

Computing Handbook Third Edition

Author: Teofilo Gonzalez
Publisher: CRC Press
ISBN: 9781439898529
Size: 11.10 MB
Format: PDF, ePub
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Computing Handbook, Third Edition: Computer Science and Software Engineering mirrors the modern taxonomy of computer science and software engineering as described by the Association for Computing Machinery (ACM) and the IEEE Computer Society (IEEE-CS). Written by established leading experts and influential young researchers, the first volume of this popular handbook examines the elements involved in designing and implementing software, new areas in which computers are being used, and ways to solve computing problems. The book also explores our current understanding of software engineering and its effect on the practice of software development and the education of software professionals. Like the second volume, this first volume describes what occurs in research laboratories, educational institutions, and public and private organizations to advance the effective development and use of computers and computing in today’s world. Research-level survey articles provide deep insights into the computing discipline, enabling readers to understand the principles and practices that drive computing education, research, and development in the twenty-first century.

Neural Symbolic Learning Systems

Author: Artur S. d'Avila Garcez
Publisher: Springer Science & Business Media
ISBN: 9781447102113
Size: 20.80 MB
Format: PDF, ePub, Docs
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Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.