This is the first book to explain Ripple-Down Rules, an approach to building knowledge-based systems which is more similar to machine learning methods than other rule-based systems but which depends on using an expert rather than applying statistics to data The book provides detailed worked examples and uses publicly available software to demonstrate Ripple-Down Rules The examples enable users to build their own RDR tools
This book constitutes the refereed proceedings of the 16th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2008, held in Acitrezza, Sicily, Italy, in September/October 2008. The 17 revised full papers and 15 revised short papers presented together with 3 invited talks were carefully reviewed and selected from 102 submissions. The papers are organized in topical sections on knowledge patterns and knowledge representation, matching ontologies and data integration, natural language, knowledge acquisition and annotations, search, query and interaction, as well as ontologies.
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.
This book constitutes the refereed proceedings of the Third Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD '99, held in Beijing, China, in April 1999. The 29 revised full papers presented together with 37 short papers were carefully selected from a total of 158 submissions. The book is divided into sections on emerging KDD technology; association rules; feature selection and generation; mining in semi-unstructured data; interestingness, surprisingness, and exceptions; rough sets, fuzzy logic, and neural networks; induction, classification, and clustering; visualization; causal models and graph-based methods; agent-based and distributed data mining; and advanced topics and new methodologies.
This book comprises the refereed proceedings of the International Conferences, ASEA and DRBC 2012, held in conjunction with GST 2012 on Jeju Island, Korea, in November/December 2012. The papers presented were carefully reviewed and selected from numerous submissions and focus on the various aspects of advanced software engineering and its applications, and disaster recovery and business continuity.
This book constitutes the proceedings of the 13th International Workshop on Knowledge Management and Acquisition for Intelligent Systems, PKAW 2014, held in Gold Cost, Qld, Australia, in December 2014. The 18 full papers and 4 short papers included in this volume were carefully reviewed and selected from 69 initial submissions. They deal with knowledge acquisition, expert systems, intelligent agents, ontology engineering, foundations of artificial intelligence, machine learning, data mining, Web mining, information systems, Web and other applications.
The papers in this volume deal with academic research topics as well as practical applications in AI. Special emphasis is given to computer vision, machine learning, neural networks mixed with theory of logic and reasoning, and practical applications of expert systems in industry and decision support.
th The 11 International Workshop on Knowledge Management and Acquisition for Smart Systems and Services (PKAW 2010) has provided a forum for the past two decades for researchers and practitioners working in the area of machine intelligence. PKAW covers a spectrum of techniques and approaches to implement smartness in IT applications. As evidenced in the papers in this volume, machine intelligence solutions incorporate many areas of AI such as ontological engineering, agent-based techn- ogy, robotics, image recognition and the Semantic Web as well as many other fields of computing such as software engineering, security, databases, the Internet, information retrieval, language technology and game technology. PKAW has evolved to embrace and foster advances in theory, practice and te- nology not only in knowledge acquisition and capture but all aspects of knowledge management including reuse, sharing, maintenance, transfer, merging, reconciliation, creation and dissemination. As many nations strive to be knowledge economies and organizations seek to maximize their knowledge assets and usage, solutions to handle the complex task of knowledge management are more important than ever. This v- ume contributes towards this goal. This volume seeks to disseminate the latest solutions from the International Wo- shop on Knowledge Management and Acquisition for Smart Systems and Services (PKAW 2010) held in Daegu, Korea during August 30–31, 2010 in conjunction with the Pacific Rim International Conference on Artificial Intelligence (PRICAI 2010).
The Machine Intelligence series was founded in 1965 by Donald Michie and has included many of the most important developments in the field over the past decades. This volume focuses on the theme of intelligent agents and features work by a number of eminent figures in artificial intelligence, including John McCarthy, Alan Robinson, Robert Kowalski, and Mike Genesereth. Topics include representations of consciousness, SoftBots, parallel implementations of logic, machine learning, machine vision, and machine-based scientific discovery in molecular biology.
This book constitutes the refereed proceedings of the 18th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2012, held in Galway City, Ireland, in October 2012. The 44 revised full papers were carefully reviewed and selected from 107 submissions. The papers are organized in topical sections on knowledge extraction and enrichment, natural language processing, linked data, ontology engineering and evaluation, social and cognitive aspects of knowledge representation, application of knowledge engineering, and demonstrations.
