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High Quality Content by WIKIPEDIA articles! Knowledge engineering (KE) was defined in 1983 by EdwardFeigenbaum, and Pamela McCorduck as follows: KE is anengineering discipline that involves integrating knowledgeinto computer systems in order to solve complex problemsnormally requiring a high level of human expertise.Atpresent, it refers to the building, maintaining anddevelopment of knowledge-based systems. It has a great dealin common with software engineering, and is used in manycomputer science domains such as artificial intelligence,including databases, data mining, expert systems, decisionsupport systems and geographic information systems.Knowledge engineering is also related to mathematical logic,as well as strongly involved in cognitive science and socio- cognitive engineering where the knowledge is produced bysocio-cognitive aggregates (mainly humans) and is structuredaccording to our understanding of how human reasoning andlogic works. ... Read more

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Purchase includes free access to book updates online and a free trial membership in the publisher's book club where you can select from more than a million books without charge. Chapters: Mit Computer Science and Artificial Intelligence Laboratory, Vattikuti Urology Institute, Livingston Robotics Club, Mobile Robotics Laboratory at Iisc, Georgia Institute of Technology Center for Robotics and Intelligent Machines, Robojackets, Chicago Engineering Design Team, Humans United Against Robots, Robotics Institute, Australian Centre for Field Robotics, European Robotics Research Network, Laboratory Robotics Interest Group, Institute for Personal Robots in Education, International Federation of Robotics, Swedish Robotics Society. Excerpt:The Australian Centre for Field Robotics (ACFR) is dedicated to the research and teaching of concepts relating to intelligent autonomous systems , at The University of Sydney in NSW, Australia. Originally established as an ARC Key Centre of Teaching and Research in 1999, it now forms part of the ARC Centre of Excellence for Autonomous Systems , along with groups at the University of Technology, Sydney and the University of New South Wales . Research Direction The Centre undertakes research in a broad range of areas related to the perception, control and learning capabilities of land, air and sea-based autonomous systems. Work at the ACFR is directed to the perception and systems aspects of this larger research area, specifically: Funding and Affiliations The Centre has close links with the Mechatronics and Intelligent Systems Group at UTS and the Artificial Intelligence and Mechatronics groups at UNSW. As an ARC Key Centre of Learning, it receives funding through the ARC from the NSW State Government. Additionally, significant linkages with and support from private enterprise result from partnerships and contracts with the Centre. These include: See also (online edition) Websites (URLs online) A hyperlinked version of this chapter is a... ... Read more

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High Quality Content by WIKIPEDIA articles! A weak ontology in computer science is one that is not sufficiently rigorous to allow software to infer new facts without an intervention by human beings (the end users of the software system). This distinction does not apply to the philosophical term, because in philosophy all inference is performed by human beings. Accordingly, from the point of view of computer science, philosophers do only weak ontology, except to the degree that their work converges with mathematics, Boolean logic, and other subfields in which automatic reasoning is known to be possible. By this standard ? which evolved as artificial intelligence methods became more sophisticated, and computers were used to model high human impact decisions ? most databases use weak ontologies. ... Read more

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Logic's basic elements are unfolded in this book. The relation of and the transition from Logic to Logic Programming are analysed.

With the use and the development of computers in the beginning of the 1950's, it soon became clear that computers could be used, not only for arithmetical computation, but also for symbolic computation. Hence, the first arithmetical computation programs, and the first programs created to answer elementary questions and prove simple theorems, were written simultaneously. The basic steps towards a general method based on Logic, were accomplished in 1965 by Robinson and later by Kowalski and Colmerauer who made use of Logic directly as a Logic Programming language.

Each chapter includes solved as well as unsolved exercises provided to help the reader assimilate the corresponding topics. The solved exercises demonstrate how to work methodically, whereas the unsolved exercises aim to stimulate the reader's personal initiative. The contents of the book are self-contained; only an elementary knowledge of analysis is required. Thus, it can be used by students in every academic year, as simply reading material, or in the context of a course. It can also be used by those who utilize Logic Programming without having any particular theoretical background knowledge of Logic, or by those simply interested in Logic and its applications in Logic Programming. ... Read more

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This series of videos was spawned from the MIT symposium, held October 1998, celebrating the 25th anniversary of the founding of Project MAC. The symposium covered the full range of computer science research ongoing at MIT's Laboratories for Computer Science and Artificial Intelligence. Leading researchers at the Labs highlighted current research and future activities in multiprocessors and parallel computer architectures; languages and systems for distributed computing; intelligent systems (AI); linguistics and robotics; cryptology, complexity and random computation theory; parallel algorithms; programming language theory; and computers and economic productivity. ... Read more

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Formal concept analysis has been developed as a field of applied mathematics based on the mathematization of concept and concept hierarchy. It thereby allows us to mathematically represent, analyze, and construct conceptual structures. The formal concept analysis approach has been proven successful in a wide range of application fields.

This book constitutes a comprehensive and systematic presentation of the state of the art of formal concept analysis and its applications. The first part of the book is devoted to foundational and methodological topics. The contributions in the second part demonstrate how formal concept analysis is successfully used outside of mathematics, in linguistics, text retrieval, association rule mining, data analysis, and economics. The third part presents applications in software engineering.

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This book offers students and AI programmers a new perspective on the study of artificial intelligence concepts. The essential topics and theory of AI are presented, but it also includes practical information on data input & reduction as well as data output (i.e., algorithm usage). Because traditional AI concepts such as pattern recognition, numerical optimization and data mining are now simply types of algorithms, a different approach is needed. This sensor / algorithm / effecter approach grounds the algorithms with an environment, helps students and AI practitioners to better understand them, and subsequently, how to apply them. The book has numerous up to date applications in game programming, intelligent agents, neural networks, artificial immune systems, and more. A CD-ROM with simulations, code, and figures accompanies the book. ... Read more

Customer Reviews (2)

Plain simple English!
I think this book is great! It explains the fundamentals of AI in a way that is easy to read and understand! I recommend this book to AI newbies who don't want to spend hours figuring out technical language. It was really useful for me!

Best AI book I've seen so far
I am just getting into AI and I've been looking for some good AI books. This is the first one I've found that covers a great selection of AI topics and talks about them in PLAIN ENGLISH instead of trying to baffle you with lots of academic gibberish! It's also liberally sprinkled with clear diagrams and programming code. So if you want a practical AI book you can actually use and understand, I think this is a good one. It's written by an AI professional who can actually WRITE and communicate clearly! What a surprise!

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