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 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.
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 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.
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."
Inhaltsangabe
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
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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