000			03665cam a22002777a 4500
999			_c28442 _d28442
001			63083
020			_a978-0857292988
020			_a9780857292988
040			_aYDXCP
082	0	4	_a006.3
100	1		_aErtel, Wolfgang _938956
245	1	0	_aIntroduction to artificial intelligence _cWolfgang Ertel
260			_aLondon ; New York : _bSpringer, _cc2011.
300			_axi, 316 p. : _bill. (some col.) ; _c24 cm.
490	1		_aUndergraduate topics in computer science
504			_aIncludes bibliographical references (p. 305-310) and index.
505	0		_a1. Introduction. -- What is artificial intelligence? -- The history of AI -- Agents -- Knowledge-based systems -- 2. Propositional Logic. -- Syntax -- Semantics -- Proof systems -- Resolution -- Horn clauses -- Computability and complexity -- Applications and limitations -- 3. First-order Predicate Logic. -- Syntax -- Semantics -- Quantifiers and normal forms -- Proof calculi -- Resolution -- Automated Theorem Provers --Mathematical examples -- Applications -- 4. Limitations of Logic. -- The search space problem -- Decidability and incompleteness -- The flying penguin -- Modeling uncertainty -- 5. Logic Programming with PROLOG. -- PROLOG systems and implementations -- Simple exercises -- Execution control and procedural elements -- Lists -- Self-modifying programs -- A planning example -- Constraint logic programming -- 6. Search, Games and Problem Solving. -- Introduction -- Uninformed search -- Heuristic search -- Games with opponents -- Heuristic evaluation functions -- State of the art -- 7. Reasoning with Uncertainty. -- Computing with probabilities -- The principle of maximum entropy -- LEXMED, and expert system for diagnosing appendicitis -- Reasoning with Bayesian networks -- 8. Machine Learning and Data Mining. -- Data analysis -- The perceptron, a linear classifier -- The nearest neighbor method -- Decision tree learning -- Learning of Bayesian networks -- The naive Bayes classifier -- Clustering -- Data mining in practice -- 9. Neural Networks. -- From biology to simulation -- Hopfield networks -- Neural Associative memory -- Linear networks with minimal errors -- The backpropagation algorithm -- Support vector machines -- Applications -- 10 Reinforcement Learning. -- Introduction -- The task -- Uninformed combinatorial search -- Value iteration and dynamic programming -- A learning walking robot and its simulation -- Q-learning -- Exploration and exploitation -- Approximation, generalization and convergence -- Applications -- Curse of dimensionality -- 11. Solutions for the Exercises. -- Introduction -- Propositional logic -- First-order predicate logic -- Limitations of logic -- PROLOG -- Search, games and problem solving -- Reasoning with uncertainty -- Machine learning and data mining -- Neural networks -- Reinforcement learning.
520			_aThis accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. It provides study exercises at the end of each chapter, plus examples, definitions, theorems, and illustrations.
526	0		_aCSCI323 CSCI236 CSCI346 CSCI356 CSCI366
650		0	_aArtificial intelligence _9370
700			_aBlack, Nathanael, _eTranslated by _938957
700			_aMast, Florian, _eIllustrations by _938958
830		0	_aUndergraduate topics in computer science. _92070
856			_uhttps://uowd.box.com/s/5tfcyofz1iagzl63whqgic1sfxmdzuij _zLocation Map
942			_cREGULAR _2ddc