Book on Hybrid Neural Systems

[Stefan.Wermter]

NEW BOOK ON HYBRID NEURAL SYSTEMS
====================================

Title: Hybrid Neural Systems

Stefan Wermter, University of Sunderland, UK
Ron Sun, University of Missouri, Columbia, MO, USA  (Eds.)


More details on this book Hybrid Neural Systems can be gained from its web
page at http://www.his.sunderland.ac.uk/ -> New Book and
http://www.his.sunderland.ac.uk/newbook/hybrid.html
(all abstracts and first chapter)

Overview
---------

Keywords: Artificial Neural Networks, Hybrid Neural Systems,
Connectionism, Hybrid Symbolic Neural Architectures,
Cognitive Neuroscience, Machine Learning, Language Processing

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
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 Hlldobler, Yvonne Kalinke and Jrg 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 Sablatng, Stefan Enderle, Gnther Palm

======================================================

Online order
--------
http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67305-9

Publisher: Springer

Publication Date: 29 March 2000

Wermter, S., University of Sunderland, UK
Sun, R., University of Missouri, Columbia, MO, USA
(Eds.)

Hybrid Neural Systems
2000. IX, 403 pp.
3-540-67305-9
DM 86,- Recommended List Price
LNCS 1778


***************************************
Professor Stefan Wermter
Research Chair in Intelligent Systems
University of Sunderland
Centre of Informatics, SCET
St Peters Way
Sunderland SR6 0DD
United Kingdom

phone: +44 191 515 3279
fax:   +44 191 515 2781
email: stefan.wermter at sunderland.ac.uk
http://www.his.sunderland.ac.uk/~cs0stw/
http://www.his.sunderland.ac.uk/
****************************************