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[Ron Sun]

A monograph on connectionist models is available 
from John Wiley and Sons, Inc. 

Title: Integrating Rules and Conenctionism for Robust Commonsense Reasoning

ISBN 0-471-59324-9
Author:   Ron Sun
          Assistant Professor
          Department of Computer Science
          The University of Alabama
          Tuscaloosa, AL 35487



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A brief description is as follows:

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