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Looking at ways to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system.Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based neurocomputing, the emphasis is on the use and representation of knowledge about an application. Explicit modeling of the knowledge represented by such a system remains a major research topic. The reason is that humans find it difficult to interpret the numeric representation of a neural network.The key assumption of knowledge-based neurocomputing is that knowledge is obtainable from, or can be represented by, a neurocomputing system in a form that humans can understand. That is, the knowledge embedded in the neurocomputing system can also be represented in a symbolic or well-structured form, such as Boolean functions, automata, rules, or other familiar ways. The focus of knowledge-based computing is on methods to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system.ContributorsC. Aldrich, J. Cervenka, I. Cloete, R.A. Cozzio, R. Drossu, J. Fletcher, C.L. Giles, F.S. Gouws, M. Hilario, M. Ishikawa, A. Lozowski, Z. Obradovic, C.W. Omlin, M. Riedmiller, P. Romero, G.P.J. Schmitz, J. Sima, A. Sperduti, M. Spott, J. Weisbrod, J.M. Zurada
This book details the state-of-the-art in knowledge-based neurocomputing. It introduces a novel fuzzy-rule base known as Fuzzy All-permutations Rule-Base (FARB) and presents new connections between artificial neural networks and FARB.
In this 2012 edition of Advances in Knowledge-Based and Intelligent Information and Engineering Systems the latest innovations and advances in Intelligent Systems and related areas are presented by leading experts from all over the world. The 228 papers that are included cover a wide range of topics. One emphasis is on Information Processing, which has become a pervasive phenomenon in our civilization. While the majority of Information Processing is becoming intelligent in a very broad sense, major research in Semantics, Artificial Intelligence and Knowledge Engineering supports the domain specific applications that are becoming more and more present in our everyday living. Ontologies play a major role in the development of Knowledge Engineering in various domains, from Semantic Web down to the design of specific Decision Support Systems. Research on Ontologies and their applications is a highly active front of current Computational Intelligence science that is addressed here. Other subjects in this volume are modern Machine Learning, Lattice Computing and Mathematical Morphology.The wide scope and high quality of these contributions clearly show that knowledge engineering is a continuous living and evolving set of technologies aimed at improving the design and understanding of systems and their relations with humans.
Knowledge Based Systems (KBS) are systems that use artificial intelligence techniques in the problem solving process. This text is designed to develop an appreciation of KBS and their architecture and to help users understand a broad variety of knowledge based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters are designed to be modular providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material being presented and to stimulate thought and discussion.
This book constitutes the refereed proceedings of the scientific track of the 7th Congress of the Italian Association for Artificial Intelligence, AI*IA 2001, held in Bari, Italy, in September 2001. The 25 revised long papers and 16 revised short papers were carefully reviewed and selected for inclusion in the volume. The papers are organized in topical sections on machine learning; automated reasoning; knowledge representation; multi-agent systems; natural language processing; perception, vision, and robotics; and planning and scheduling.
This book constitutes the refereed proceedings of the scientific track of the 7th Congress of the Italian Association for Artificial Intelligence, AI*IA 2001, held in Bari, Italy, in September 2001. The 25 revised long papers and 16 revised short papers were carefully reviewed and selected for inclusion in the volume. The papers are organized in topical sections on machine learning; automated reasoning; knowledge representation; multi-agent systems; natural language processing; perception, vision, and robotics; and planning and scheduling.
Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.
Annotation The book contains the Proceedings of KES 2002, the Sixth Edition of the Knowledge-Based Intelligent Information & Engineering Systems International Conference. The conference papers presented new research results, focusing on three main areas of interest: Generic Intelligent Techniques: This area includes results on basic disciplines underlying knowledge-based and intelligent systems, such as artificial neural networks, machine learning, knowledge-based systems, case-based reasoning, intelligent agents and soft computing. Applications of Intelligent Systems: The second area presents results on vertical applications of intelligent systems, including condition monitoring, fault diagnosis, industrial control, medical systems, image processing, financial & stock market monitoring and prediction, natural language processing and others. Allied Technologies: This area includes novel contributions on intelligent systems' applications to traditional research fields such as digital and computer communications, signal processing, virtual reality, multi-media, web-based technologies, human-computer interfaces and software engineering."
This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).
Papers presented at the 2003 Neural Information Processing Conference by leading physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees -- physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2003 conference.