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.
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