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An authoritative and accessible one-stop resource, the first edition of An Introduction to Artificial Intelligence presented the first full examination 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 modeling. 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 added to existing chapters, including short case studies, as well as adding new chapters on explainable AI, and big data. It expands the book's focus on human-centred AI, covering bias (gender, ethnic), the need for transparency, augmentation vs replacement, IUI, and designing interactions to aid ML. 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/.
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An authoritative and accessible one-stop resource, the first edition of An Introduction to Artificial Intelligence presented the first full examination 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 modeling. 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 added to existing chapters, including short case studies, as well as adding new chapters on explainable AI, and big data. It expands the book's focus on human-centred AI, covering bias (gender, ethnic), the need for transparency, augmentation vs replacement, IUI, and designing interactions to aid ML. 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/.