Proceedings Of The European Knowledge Acquisition Workshop Ekaw 88 June 19 23 1988

Artificial Intelligence is one of the oldest and most exciting subfields of computing, covnering such areas as intelligent robotics, intelligent planning and scheduling, model-based reasoning, fault diagnosis, natural language processing, maching translation, knowledge representation and reasoning, knowledge-based systems, knowledge engineering, intelligent agents, machine learning, neural nets, genetic algorithms and knowledge management. The papers in this volume comprise the refereed proceedings of the Second International Conference on Artificial Intelligence Applications and Innovations,held in Beijing, China in 2005. A very promising sign of the growing importance of Artificial Intelligence techniques in practical applications is the large number of submissions received for the conference - more than 150. All papers were reviewed by at least two members of the Program Committee and the test 93 were selected for the conference and are included in this volume. The international nature of IFIP is amply reflected in the large number of countries represented here.

Presents the latest advancements in cognitive informatics and natural intelligence. Covers the five areas of cognitive informatics, natural intelligence, autonomic computing, knowledge science, and relevant development.

Knowledge acquisition has become a major area of artificial intelligence and cognitive science research. The papers in this book show that the area of knowledge acquisition for knowledge-based systems is still a diverse field in which a large number of research topics are being addressed. However, several main themes run through the papers. First, the issues of integrating knowledge from different sources and K.A. tools is a salient topic in many papers. A second major topic in the papers is that of knowledge modelling. Research in knowledge-based systems emphasises the use of generic models of reasoning and its underlying knowledge. An important trend in the area of knowledge modelling aims at the formalisation of knowledge models. Where the field of knowledge acquisition was without tools and techniques years ago, now there is a rapidly growing body of techniques and tools. Apart from the integrated workbenches already mentioned above, several papers in this book present new tools. Although knowledge acquisition and machine learning have been considered as separate subfields of AI, there is a tendency for the two fields to come together. This publication combines machine learning techniques with more conventional knowledge elicitation techniques. A framework is presented in which reasoning, problem solving and learning together form a knowledge intensive system that can acquire knowledge from its own experience.