Hybrid Neural Systems

edited by
Stefan Wermter and Ron Sun

published by Springer, Heidelberg
March 2000

The aim of this book is to present a broad spectrum of current research in hybrid neural systems, and advance the state of the art in neural networks and artificial intelligence. Hybrid neural systems are computational systems which are based mainly on artificial neural networks but which also allow a symbolic interpretation or interaction with symbolic components.

This book focuses on the following issues related to different types of representation: How does neural representation contribute to the success of hybrid systems? How does symbolic representation supplement neural representation? How can these types of representation be combined? How can we utilize their interaction and synergy? How can we develop neural and hybrid systems for new domains? What are the strengths and weaknesses of hybrid neural techniques? Are current principles and methodologies in hybrid neural systems useful? How can they be extended? What will be the impact of hybrid and neural techniques in the future?

Table of Contents

An Overview of Hybrid Neural Systems (PS) (PDF)
Stefan Wermter and Ron Sun

Structured Connectionism and Rule Representation

Layered Hybrid Connectionist Models for Cognitive Science
Jerome Feldman and David Bailey

Types and Quantifiers in SHRUTI --- A Connectionist Model of Rapid Reasoning and Relational Processing
Lokendra Shastri

A Recursive Neural Network for Reflexive Reasoning
Steffen Hölldobler, Yvonne Kalinke and Jörg Wunderlich

A Novel Modular Neural Architecture for Rule-based and Similarity-based Reasoning
Rafal Bogacz and Christophe Giraud-Carrier

Addressing Knowledge-Representation Issues in Connectionist Symbolic Rule Encoding for General Inference
Nam Seog Park

Towards a Hybrid Model of First-Order Theory Refinement
Nelson A. Hallack, Gerson Zaverucha and Valmir C. Barbosa

Distributed Neural Architectures and Language Processing

Dynamical Recurrent Networks for Sequential Data Processing
Stefan Kremer and John Kolen

Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective
Christian W. Omlin, Lee Giles and Karvel K. Thornber

Combining Maps and Distributed Representations for Shift-Reduce Parsing
Marshall R. Mayberry and Risto Miikkulainen

Towards Hybrid Neural Learning Internet Agents
Stefan Wermter, Garen Arevian and Christo Panchev

A Connectionist Simulation of the Empirical Acquisition of Grammatical Relations
William C. Morris, Garrison W. Cottrell and Jeffrey L. Elman

Large Patterns Make Great Symbols: An Example of Learning from Example
Pentti Kanerva

Context Vectors: A Step Toward a Grand Unified Representation
Stephen I. Gallant

Integration of Graphical Rules with Adaptive Learning of Structured Information
Paolo Frasconi, Marco Gori and Alessandro Sperduti

Transformation and Explanation

Lessons from Past, Current Issues and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks
Alan B. Tickle, Frederic Maire, Guido Bologna, Robert Andrews and Joachim Diederich

Symbolic Rule Extraction from the DIMLP Neural Network
Guido Bologna

Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics
Peter Tino, Georg Dorffner and Christian Schittenkopf

Direct Explanations and Knowledge Extraction from a Multilayer Perceptron Network that Performs Low Back Pain Classification
Marilyn L. Vaughn, Steven J. Cavill, Stewart J. Taylor, Michael A. Foy and Anthony J.B. Fogg

High Order Eigentensors as Symbolic Rules in Competitive Learning
Hod Lipson and Hava T. Siegelmann

Holistic Symbol Processing and the Sequential RAAM: An Evaluation
James A. Hammerton and Barry L. Kalman

Robotics, Vision and Cognitive Approaches

Life, Mind and Robots: The Ins and Outs of Embodied Cognition
Noel Sharkey and Tom Ziemke

Supplementing Neural Reinforcement Learning with Symbolic Methods
Ron Sun

Self-Organizing Maps in Symbol Processing
Timo Honkela

Evolution of Symbolisation: Signposts to a Bridge between Connectionist and Symbolic Systems
Ronan Reilly

A Cellular Neural Associative Array for Symbolic Vision
Christos Orovas and James Austin

Application of Neurosymbolic Integration for Environment Modelling in Mobile Robots
Gerhard K. Kraetzschmar, Stefan Sablatnög, Stefan Enderle, Günther Palm

Hybrid Neural Systems can be ordered from Springer-Verlag by using the on-line Order Form.



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