Books and Special Issues
Hybrid Neural Systems BookStefan Wermter and Ron SunMarch 2000, Springer, HeidelbergThe 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. |
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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? |
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Chapters
An Overview of Hybrid Neural Systems (Abstract) Full Chapter (PS) Full Chapter (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 Spring-Verlag
by using the on-line Order
Form.
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Contact
Prof. Stefan Wermter Phone: +49 40 428 83 2434 |
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