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Author:   Paul Compton (The University of New South Wales, Syndey, Australia) ,  Byeong Ho Kang (University of Tasmania, Tasmania, Australia)
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Weight:   0.660kg
ISBN:  

9780367644321

ISBN 10:   0367644320
Pages:   196
Publication Date:   31 May 2021
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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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

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

Author Information

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.""

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