Introduction to artificial intelligence / Wolfgang Ertel ; translated by Nathanael Black ; with illustrations by Florian Mast.
Material type: TextSeries: Undergraduate topics in computer sciencePublication details: Cham, Switzerland : Springer, 2017. ©2017Edition: 2nd edDescription: xiv, 356 p. : ill. (some col.) ; 24 cmISBN: 9783319584867 (pbk.); 3319584863 (pbk.)Subject(s): Artificial intelligenceLOC classification: Q335 | .E78 2017Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
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General Circulation | APU Library Open Shelf | Book | Q335 .E78 2017 c.1 (Browse shelf (Opens below)) | 1 | Available | 00012340 |
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Q335 .C43 1985 c.3 Introduction to artificial intelligence / | Q335 .D43 1995 c.1 Artificial intelligence : | Q335 .D85 2018 c.1 Why AI hallucinates : the Bot-Verse begins / | Q335 .E78 2017 c.1 Introduction to artificial intelligence / | Q335 .F56 1996 c.1 An introduction to artificial intelligence / | Q335 .F56 1996 c.2 An introduction to artificial intelligence / | Q335 .G46 1987 c.1 Logical foundations of artificial intelligence / |
Includes bibliographical references (pages 339-349) and index.
Introduction -- Propositional Logic -- First-order Predicate Logic -- Limitations of Logic -- Logic Programming with PROLOG -- Search, Games and Problem Solving -- Reasoning with Uncertainty -- Machine Learning and Data Mining -- Neural Networks -- Reinforcement Learning -- Solutions for the Exercises.
"This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated second edition also includes new material on deep learning. Topics and features: Presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website; contains numerous study exercises and solutions, highlighted examples, definitions, theorems, and illustrative cartoons;iIncludes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; reports on developments in deep learning, including applications of neural networks to generate creative content such as text, music and art (NEW); examines performance evaluation of clustering algorithms, and presents two practical examples explaining Bayestheorem and its relevance in everyday life (NEW); discusses search algorithms, analyzing the cycle check, explaining route planning for car navigation systems, and introducing Monte Carlo Tree Search (NEW); Includes a section in the introduction on AI and society, discussing the implications of AI on topics such as employment and transportation (NEW). Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material."--Back cover.
Translated from the German.
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