Ripple-Down Rules: the Alternative to Machine Learning

Machine learning algorithms hold 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 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. RDR is based on this more situated understanding of knowledge. The central features of a 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 idea of the simple yet counter-intuitive RDR algorithms to easily build their own RDR systems. It has been proven in industrial applications 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 on its own or to improve the performance of machine learning or other methods.

PrefaceAcknowledgements1 Problems with Machine Learning and Knowledge Acquisition1.1 Introduction1.2 Machine Learning1.3 Knowledge Acquisition2 Philosophical issues in knowledge acquisition3 Ripple-Down Rule Overview3.1 Case-driven knowledge acquisition3.2 Order of cases processed3.3 Linked Production Rules3.4 Adding rules3.5 Assertions and retractions3.6 Formulae in conclusion4 Introduction to Excel_RDR5 Single Classification Example5.1 Repetition in an SCRDR knowledge base5.2 SCRDR evaluation and machine learning comparison5.3 Summary6 Multiple classification example6.1 Introduction to Multiple Classification Ripple-Down Rules (MCRDR)6.2 Excel_MCRDR example6.3 Discussion: MCRDR for single classification6.4 Actual Multiple classification with MCRDR6.5 Discussion6.6 Summary7 General Ripple-Down Rules (GRDR)7.1 Background7.2 Key Features of GRDR7.3 Excel_GRDR demo7.4 Discussion: GRDR, MCRDR and SCRDR8 Implementation and Deployment of RDR-based systems8.1 Validation8.2 The role of the user/expert8.3 Cornerstone Cases8.4 Explanation_8.5 Implementation Issues8.6 Information system interfaces9 RDR and Machine learning9.1 Suitable datasets9.2 Human experience versus statistics. 9.3 Unba…