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Ripple-Down Rules
The Alternative to Machine Learning




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ISBN 9780367644321
March 25, 2021 Forthcoming by Chapman and Hall/CRC
232 Pages 112 Color Illustrations

 
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Book Description

Machine learning algorithms hold out extraordinary promise, but the reality is that their success depends entirely on the suitability of the data available. This book is about Ripple-Down Rules (RDR) an alternative manual technique for rapidly building AI systems. With a human in the loop, RDR is much better able to deal with the limitations of the data.

Ripple-Down Rules: The Alternative to Machine Learning starts by reviewing the problems with data quality, and the problems with conventional approaches to incorporating expert human knowledge into AI systems. It suggests that problems with knowledge acquisition arise because of mistaken philosophical assumptions about knowledge. It argues people never really explain how they reach a conclusion, rather they justify their conclusion by differentiating between cases in a context, and RDR is based on this more situated understanding of knowledge.  The central features of an RDR approach are explained, and detailed worked examples are presented for different types of RDR, based on freely available software developed for this book. The examples ensure developers have a clear enough idea of the simple yet counter-intuitive RDR algorithms to easily build their own RDR systems.

It has been proven in industrial application that it takes only a minute or two per rule to build RDR systems with perhaps thousands of rules. The industrial uses of RDR have ranged from medical diagnosis, through data cleansing to chatbots in cars. RDR can be used standalone or to improve the performance of machine learning or other methods.

Table of Contents

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

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Author(s)

Biography

Paul Compton initially studied philosophy before majoring in physics.  He spent 20 years as a biophysicist at the Garvan Institute of Medical Research, and then 20 years in Computer Science and Engineering at the University of New South Wales, where he was head of school for 12 years and is now an emeritus professor.

Byeong Ho Kang majored in mathematics in Korea, followed by a PhD on Ripple-Down Rules at the University of New South Wales and the algorithm he developed is the basis of most industry RDR applications. He is a professor, with a research focus on applications, and head of the ICT discipline at the University of Tasmania."

Reviews

"In this era of deep learning, where is our deeper understanding of AI? The answer is, here,  in this book.  Compton and Kang's ideas are a "must-read" for anyone working with AI. Based on very many examples of real-world applications, they show us a better way to use AI. If your AI models are confusing to understand and hard to maintain, then this book is for you."

-- Tim Menzies, Professor, North Carolina State University