Research And Development In Intelligent Systems Xviii
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Research and Development in Intelligent Systems XVIII
Author: Frans Coenen
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
M.A. BRAMER University of Portsmouth, UK This volume comprises the refereed technical papers presented at ES200 l, the Twenty-fIrst SGES International Conference on Knowledge Based Systems and Applied ArtifIcial Intelligence, held in Cambridge in December 200 l, together with an invited keynote paper by Professor Derek Sleeman. The conference was organised by SGES, the British Computer Society Specialist Group on Knowledge Based Systems and Applied ArtifIcial Intelligence. The papers in this volume present new and innovative developments in the fIeld, divided into sections on Machine Learning, Constraint Satisfaction, Agents, Knowledge Representation, Knowledge Engineering, and Intelligent Systems. The refereed papers begin with a paper entitled 'Detecting Mismatches Among Experts' Ontologies Acquired Through Knowledge Elicitation', which describes a systematic approach to the analysis of discrepancies within and among experts' ontologies. This paper was judged to be the best refereed technical paper submitted to the conference. The remaining papers are devoted to topics in important areas such as agents, knowledge engineering, knowledge representation, planning and constraint satisfaction, with machine learning again the largest topic covered in terms of the number of papers accepted for publication. This is the eighteenth volume in the Research and Development series. The Application Stream papers are published as a companion volume under the title Applications and Innovations in Intelligent Systems IX.
Intelligent Systems for Engineers and Scientists
The fourth edition of this bestselling textbook explains the principles of artificial intelligence (AI) and its practical applications. Using clear and concise language, it provides a solid grounding across the full spectrum of AI techniques, so that its readers can implement systems in their own domain of interest. The coverage includes knowledge-based intelligence, computational intelligence (including machine learning), and practical systems that use a combination of techniques. All the key techniques of AI are explained—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), agents, objects, frames, symbolic learning, case-based reasoning, genetic algorithms and other optimization techniques, shallow and deep neural networks, hybrids, and the Lisp, Prolog, and Python programming languages. The book also describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. Fully updated and revised, Intelligent Systems for Engineers and Scientists: A Practical Guide to Artificial Intelligence, Fourth Edition features: A new chapter on deep neural networks, reflecting the growth of machine learning as a key technique for AI A new section on the use of Python, which has become the de facto standard programming language for many aspects of AI The rule-based and uncertainty-based examples in the book are compatible with the Flex toolkit by Logic Programming Associates (LPA) and its Flint extension for handling uncertainty and fuzzy logic. Readers of the book can download this commercial software for use free of charge. This resource and many others are available at the author’s website: adrianhopgood.com. Whether you are building your own intelligent systems, or you simply want to know more about them, this practical AI textbook provides you with detailed and up-to-date guidance.
Intelligent Systems for Engineers and Scientists, Third Edition
The third edition of this bestseller examines the principles of artificial intelligence and their application to engineering and science, as well as techniques for developing intelligent systems to solve practical problems. Covering the full spectrum of intelligent systems techniques, it incorporates knowledge-based systems, computational intelligence, and their hybrids. Using clear and concise language, Intelligent Systems for Engineers and Scientists, Third Edition features updates and improvements throughout all chapters. It includes expanded and separated chapters on genetic algorithms and single-candidate optimization techniques, while the chapter on neural networks now covers spiking networks and a range of recurrent networks. The book also provides extended coverage of fuzzy logic, including type-2 and fuzzy control systems. Example programs using rules and uncertainty are presented in an industry-standard format, so that you can run them yourself. The first part of the book describes key techniques of artificial intelligence—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), frames, objects, agents, symbolic learning, case-based reasoning, genetic algorithms, optimization algorithms, neural networks, hybrids, and the Lisp and Prolog languages. The second part describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. The author provides sufficient detail to help you develop your own intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, this book provides you with detailed and up-to-date guidance. Check out the significantly expanded set of free web-based resources that support the book at: http://www.adrianhopgood.com/aitoolkit/