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010 _a 2019753363
020 _a9783319584874
024 7 _a10.1007/978-3-319-58487-4
_2doi
035 _a(DE-He213)978-3-319-58487-4
040 _aDLC
_beng
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072 7 _aCOM004000
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082 0 4 _a005
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100 1 _aErtel, Wolfgang,
_eauthor.
_9466
245 1 0 _aIntroduction to Artificial Intelligence /
_cby Wolfgang Ertel.
250 _a2nd ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _a1 online resource (XIV, 356 pages 130 illustrations, 46 illustrations in color.)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUndergraduate Topics in Computer Science,
_x1863-7310
505 0 _aIntroduction -- 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.
520 _aThis 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 Includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learn ing 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 Bayes' theorem 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. Dr. Wolfgang Ertel is a professor at the Institute for Artificial Intelligence at the Ravensburg-Weingarten University of Applied Sciences, Germany.
588 _aDescription based on publisher-supplied MARC data.
650 0 _aArtificial intelligence.
_9467
650 1 4 _aArtificial Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
_9468
776 0 8 _iPrint version:
_tIntroduction to artificial intelligence.
_z9783319584867
_w(DLC) 2017943187
776 0 8 _iPrinted edition:
_z9783319584867
776 0 8 _iPrinted edition:
_z9783319584881
830 0 _aUndergraduate Topics in Computer Science,
_x1863-7310
_9469
906 _a0
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