**** Book Announcement ****

Integrating Rules and Conenctionism for Robust Commonsense Reasoning

Author: Ron Sun

Publisher: John Wiley and Sons, Inc. 

605 Third Ave.
New York, NY 10158-0012 USA
(212) 850-6589 FAX: (212) 850-6088

(ISBN 0-471-59324-9)




One of the outstanding problems for artificial intelligence isthe problem of better modeling commonsense reasoningand alleviating brittleness of traditional symbolic rule-based models.This work tackles this problem by trying to combining rules withconnectionist models in an integrated framework.This idea leads to the development of a connectionistarchitecture with dual representation combining symbolic and subsymbolic(feature-based) processing for evidential robust reasoning: CONSYDERR.Reasoning data are analyzed based on the notions of rules and similarity and modeled by the architecture which carries outrule application and similarity matching through interaction of the two levels;formal analyses are performed to understand rule encoding in connectionistmodels, in order to prove that it handles a superset of Horn clause logic anda nonmonotonic logic; the notion of causality is explored for the purposeof clarifying how the proposed architecture can better capture commonsensereasoning, and it is shown that causal knowledge can be well represented by CONSYDERR and utilized in reasoning, which further justifies the designof the architecture; the variable binding problem is addressed, and a solutionis proposed within this architecture and is shown to surpass existing ones;several aspects of the architecture are discussed to demonstrate howconnectionist models can supplement, enhance, and integrate symbolicrule-based reasoning; large-scale application-oriented systems are prototyped.This architecture utilizes the synergy resulting from the interaction ofthe two different types of representation and processing, and is thereforecapable of handling a large number of difficult issues in one integratedframework, such as partial and inexact information, cumulative evidentialcombination, lack of exact match, similarity-based inference, inheritance,and representational interactions, all of which are proven to be crucialelements of commonsense reasoning. The results show that connectionismcoupled with symbolic processing capabilities can be effective andefficient 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`



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