COMPUTATIONAL ARCHITECTURES INTEGRATING NEURAL AND SYMBOLIC PROCESSES: A
PERSPECTIVE ON THE STATE OF THE ART
Edited by Ron Sun and Larry Bookman
ISBN 0-7923-9517-4
(Order information is in the end of this message)
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The focus of this book is on a currently emerging body of research ---
computational architectures integrating neural and symbolic processes.
There has been a great deal of work in integrating neural and
symbolic processes, both from a cognitive and/or applicational viewpoint,
The editors of this book intend to address the underlying
architectural aspects of this integration. In order to provide a basis for a
deeper understanding of existing divergent approaches and provide insight for
further developments in this field, the book presents (1) an examination of
specific architectures (grouped together according to their approaches), their
strengths and weaknesses, why they work, and what they predict, and (2) a
critique/comparison of these approaches.
The book will be of use to
researchers, graduate students, and interested laymen, in areas such as
cognitive science, artificial intelligence, computer science, cognitive
psychology, and neurocomputing, in keeping up to date with the newest research
trends. It can also serve as a comprehensive, in-depth introduction to this
new emerging field. A unique feature of the book is a comprehensive
bibliography at the end of the book.
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TABLE OF CONTENTS
Foreword
by Michael Arbib
Preface
by Ron Sun and Larry Bookman
Chapter 1 An Introduction: On Symbolic Processing in Neural Networks
by Ron Sun
Introduction
Brief Review
Existing Approaches
Issues band Difficulties
Future Directions, Or Where Should We Go From Here?
Overview of the Chapters
Summary
Part I Localist Architectures
Chapter 2 Complex Symbol-Processing in Conposit, A Transiently Localist
Connectionist Architecture by John A. Barnden
Introduction
The Johnson-Laird Theory and Its Challenges
Mental Models in Conposit
Connectionist Realization of Conposit
Coping with the Johnson-Laird Challenge
Simulation Runs
Discussion
Summary
Chapter 3 A Structured Connectionist Approach to Inferencing and
Retrieval by Trent E. Lange
Introduction
Language Understanding and Memory Retrieval Models
Inferencing in ROBIN
Episodic Retrieval in REMIND
Future Work
Summary
Chapter 4 Hierarchical Architectures for Reasoning
by R.C. Lacher and K.D. Nguyen
Introduction
Computational Networks: A General Setting for Distributed
Computations
Type x00 Computational Networks
Expert Systems
Expert Networks
Neural Networks
Summary
Part II Distributed Architectures
Chapter 5 Subsymbolic Parsing of Embedded Structures by Risto Miikkulainen
Introduction
Overview of Subsymbolic Sentence Processing
The SPEC Architecture
Experiments
Discussion
Summary
Chapter 6 Towards Instructable Connectionist Systems
by David C. Noelle and Garrison W. Cottrell
Introduction
Systematic Action
Linguistic Interaction
Learning By Instruction
Summary
Chapter 7 An Internal Report for Connectionists
by Noel E. Sharkey and Stuart A. Jackson
Introduction
The Origins of Connectionist Representation
Representation and Decision Space
Discussion
Summary
Part III Combined Architectures
Chapter 8 A Two-Level Hybrid Architecture for Structuring Knowledge for
Commonsense Reasoning
by Ron Sun
Introduction
Developing A Two-Level Architecture
Fine-Tuning the Structure
Experiments
Comparisons with Other Approaches
Summary
Chapter 9 A Framework for Integrating Relational and Associational
Knowledge for Comprehension by Lawrence A. Bookman
Introduction
Overview of LeMICON
Text Comprehension
Encoding Semantic Memory
Representation of Semantic Constraints
Experiments and Results
Algorithm
Summary
Chapter 10 Examining a Hybrid Connectionist/Symbolic System for the
Analysis of Ballistic Signals by Charles Lin and James Hendler
Introduction
Related Work in Hybrid Systems
Description of the SCRuFFY Architecture
Analysis of Ballistic Signals
Future Work
Conclusion
Part IV Commentaries
Chapter 11 Symbolic Artificial Intelligence and Numeric Artificial Neural
Networks: Towards a Resolution of the Dichotomy
by Vasant Honavar
Introduction
Shared Foundations of SAI and NANN
Knowledge Representation Revisited
A Closer Look at SAI and NANN
Integration of SAI and NANN
Summary
Chapter 12 Connectionist Natural Language Processing: A Status Report
by Michael G. Dyer
Introduction
Dynamic Bindings
Functional Bindings and Structured Pattern Matching
Encoding and Accessing Recursive Structures
Forming Lexical Memories
Forming Semantic and Episodic Memories
Role of Working Memory
Routing and Control
Grounding Language in Perception
Future Directions
Conclusions
Appendix Bibliography of Connectionist Models with Symbolic Processing
Author Index
Subjct Index
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To order:
ISBN 0-7923-9517-4
Kluwer, Order Dept.
P.O.B. 358
Accord Station, Hingham, MA 02018-0358
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Date: Tue, 13 Sep 1994 15:28:38 -0500
From: rsun@cs.ua.edu (Ron Sun)
**** Book Announcement ****
Title: Integrating Rules and Conenctionism for Robust Commonsense Reasoning
(ISBN 0-471-59324-9)
Author: Ron Sun
Department of Computer Science
The University of Alabama
Tuscaloosa, AL 35487
Publisher: John Wiley and Sons, Inc. 1-800-call-wiley
605 Third Ave.
New York, NY 10158-0012 USA
(212) 850-6589 FAX: (212) 850-6088
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One of the outstanding problems for artificial intelligence is
the problem of better modeling commonsense reasoning
and alleviating brittleness of traditional symbolic rule-based models.
This work tackles this problem by trying to combining rules with
connectionist models in an integrated framework.
This idea leads to the development of a connectionist
architecture with dual representation combining symbolic and subsymbolic
(feature-based) processing for evidential robust reasoning: {\sc CONSYDERR}.
Reasoning data are analyzed based on the notions of {\it rules} and
{\it similarity} and modeled by the architecture which carries out
rule application and similarity matching through interaction of the two levels;
formal analyses are performed to understand rule encoding in connectionist
models, in order to prove that it handles a superset of Horn clause logic and
a nonmonotonic logic; the notion of causality is explored for the purpose
of clarifying how the proposed architecture can better capture commonsense
reasoning, and it is shown that causal knowledge can be well represented by
{\sc CONSYDERR} and utilized in reasoning, which further justifies the design
of the architecture; the variable binding problem is addressed, and a solution
is proposed within this architecture and is shown to surpass existing ones;
several aspects of the architecture are discussed to demonstrate how
connectionist models can supplement, enhance, and integrate symbolic
rule-based reasoning; large-scale application-oriented systems are prototyped.
This architecture utilizes the synergy resulting from the interaction of
the two different types of representation and processing, and is therefore
capable of handling a large number of difficult issues in one integrated
framework, such as partial and inexact information, cumulative evidential
combination, lack of exact match, similarity-based inference, inheritance,
and representational interactions, all of which are proven to be crucial
elements of commonsense reasoning. The results show that connectionism
coupled with symbolic processing capabilities can be effective and
efficient models of reasoning for both theoretical and practical purposes.
Table of Content:
1 Introduction
1.1 Overview
1.2 Commonsense Reasoning
1.3 The Problem of Common Reasoning Patterns
1.4 What is the Point?
1.5 Some Clarifications
1.6 The Organization of the Book
1.7 Summary
2 Accounting for Commonsense Reasoning: A Framework with Rules and Similarities
2.1 Overview
2.2 Examples of Reasoning
2.3 Patterns of Reasoning
2.4 Brittleness of Rule-Based Reasoning
2.5 Towards a Solution
2.6 Some Reflections on Rules and Connectionism
2.7 Summary
3 A Connectionist Architecture for Commonsense Reasoning
3.1 Overview
3.2 A Generic Architecture
3.3 Fine-Tuning --- from Constraints to Specifications
3.4 Summary
3.5 Appendix
4 Evaluations and Experiments
4.1 Overview
4.2 Accounting for the Reasoning Examples
4.3 Evaluations of the Architecture
4.4 Systematic Experiments
4.5 Choice, Focus and Context
4.6 Reasoning with Geographical Knowledge
4.7 Applications to Other Domains
4.8 Summary
4.9 Appendix: Determining Similarities and CD representations
5 More on the Architecture: Logic and Causality
5.1 Overview
5.2 Causality in General
5.3 Shoham's Causal Theory
5.4 Defining FEL
5.5 Accounting for Commonsense Causal Reasoning
5.6 Determining Weights
5.7 Summary
5.8 Appendix: Proofs For Theorems
6 More on the Architecture: Beyond Logic
6.1 Overview
6.2 Further Analysis of Inheritance
6.3 Analysis of Interaction in Representation
6.4 Knowledge Acquisition, Learning, and Adaptation
6.5 Summary
7 An Extension: Variables and Bindings
7.1 Overview
7.2 The Variable Binding Problem
7.3 First-Order FEL
7.4 Representing Variables
7.5 A Formal Treatment
7.6 Dealing with Difficult Issues
7.7 Compilation
7.8 Correctness
7.9 Summary
7.10 Appendix
8 Reviews and Comparisons
8.1 Overview
8.2 Rule-Based Reasoning
8.3 Case-Based Reasoning
8.4 Connectionism
8.5 Summary
9 Conclusions
9.1 Overview
9.2 Some Accomplishments
9.3 Lessons Learned
9.4 Existing Limitations
9.5 Future Directions
9.6 Summary
References
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