Ripple-Down Rules

Preface

Acknowledgements

1 Problems with Machine Learning and Knowledge Acquisition

1.1 Introduction

1.2 Machine Learning

1.3 Knowledge Acquisition

2 Philosophical issues in knowledge acquisition

3 Ripple-Down Rule Overview

3.1 Case-driven knowledge acquisition

3.2 Order of cases processed

3.3 Linked Production Rules

3.4 Adding rules

3.5 Assertions and retractions

3.6 Formulae in conclusion

4 Introduction to Excel_RDR

5 Single Classification Example

5.1 Repetition in an SCRDR knowledge base

5.2 SCRDR evaluation and machine learning comparison

5.3 Summary

6 Multiple classification example

6.1 Introduction to Multiple Classification Ripple-Down Rules (MCRDR)

6.2 Excel_MCRDR example

6.3 Discussion: MCRDR for single classification

6.4 Actual Multiple classification with MCRDR

6.5 Discussion

6.6 Summary

7 General Ripple-Down Rules (GRDR)

7.1 Background

7.2 Key Features of GRDR

7.3 Excel_GRDR demo

7.4 Discussion: GRDR, MCRDR and SCRDR

8 Implementation and Deployment of RDR-based systems

8.1 Validation

8.2 The role of the user/expert

8.3 Cornerstone Cases

8.4 Explanation_

8.5 Implementation Issues

8.6 Information system interfaces

9 RDR and Machine learning

9.1 Suitable datasets

9.2 Human experience versus statistics.

9.3 Unbalanced Data

9.4 Prudence

9.5 RDR-based machine learning methods

9.6 Machine learning combined with RDR knowledge acquisition

9.7 Machine learning supporting RDR

9.8 Summary_

Appendix 1 - Industrial Applications of RDR

A1.1 PEIRS (1991-1995)

A1.2 Pacific Knowledge Systems

A1.3 Ivis

A1.4 Erudine Pty Ltd

A1.5 Ripple-Down Rules at IBM

A1.6 YAWL

A1.7 Medscope

A1.8 Seegene

A1.9 IPMS

A1.10 Tapacross

Appendix 2 - Research-demonstrated Applications

A2.1 RDR Wrappers

A2.2 Text-processing, natural language processing and information retrieval

A2.3 Conversational agents and help desks

A2.4 RDR for operator and parameter selection

A2.5 Anomaly and event detection

A2.6 RDR for image and video processing

References

Index