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/.

�ber die Autorin bzw. den Autor

Alan Dix is Director of the Computational Foundry at Swansea University, a 30 million pound initiative to boost computational research in Wales with a strong focus on creating social and economic benefit. Previously Alan has worked in a mix of academic, commercial and government roles. Alan is principally known for his work in human-computer interaction, and is the author of one of the major international textbooks on HCI as well as of over 450 research publications from formal methods to intelligent interfaces and design creativity. Technically, he works equally happily with AI and machine learning alongside traditional mathematical and statistical techniques. He has a broad understanding of mathematical, computational and human issues, and he authored some of the earliest papers on gender and ethnic bias in black box-algorithms.

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