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An authoritative and accessible one-stop resource, the first edition of An Introduction to Artificial Intelligence presented one of the first comprehensive examinations of AI. Designed to provide an understanding of the foundations of artificial intelligence, it examined the central computational techniques employed by AI, including knowledge representation, search, reasoning and learning, as well as the principal application domains of expert systems, natural language, vision, robotics, software agents and cognitive modelling. Many of the major philosophical and ethical issues of AI were also introduced. This new edition expands and revises the book throughout, with new material to augment existing chapters, including short case studies, as well as adding new chapters on explainable AI, big data and deep learning, temporal and web-scale data, statistical methods and data wrangling. It expands the book’s focus on human-centred AI, covering gender, ethnic and social bias, the need for transparency, intelligent user interfaces, and designing interactions to aid machine learning. With detailed, well-illustrated examples and exercises throughout, this book provides a substantial and robust introduction to artificial intelligence in a clear and concise coursebook form. It stands as a core text for all students and computer scientists approaching AI. You can also visit the author website for further resources: https://alandix.com/aibook/.
This book provides a comprehensive examination of the foundations and applications of artificial intelligence. The text covers central computational techniques such as knowledge representation, search, reasoning, and learning, alongside key application domains like expert systems, natural language processing, vision, robotics, and cognitive modeling. It also addresses significant philosophical and ethical issues within AI, with new material on explainable AI, big data, deep learning, and human-centered AI considerations like bias and transparency.
This revised edition builds upon the established reputation of its predecessor as an authoritative and accessible resource for AI. The inclusion of new chapters on contemporary topics like explainable AI, big data, and deep learning, alongside a strengthened focus on human-centered AI, indicates an effort to keep the text current with the field's rapid advancements. The book's structure, featuring detailed examples and exercises, positions it as a core text for students and computer scientists.
Page Count:
428
Publication Date:
2025-06-16
Publisher:
Taylor & Francis
ISBN-10:
1040298761
ISBN-13:
9781040298763
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