MARC details
| 000 -LEADER |
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| fixed length control field |
09865cam a22003618a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
16512221 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20210427125844.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
101021s2011 enk b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
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| LC control number |
2010041988 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781408225745 (pbk.) |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
DLC |
| Transcribing agency |
DLC |
| Modifying agency |
EG-NcFUE |
| Description conventions |
rda |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA76.76.E95 |
| Item number |
N445 2011 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.3 |
| Edition number |
22 |
| Item number |
N.M.A |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Negnevitsky, Michael. |
| 9 (RLIN) |
33120 |
| 245 10 - TITLE STATEMENT |
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| Title |
Artificial intelligence : |
| Remainder of title |
a guide to intelligent systems / |
| Statement of responsibility, etc |
Michael Negnevitsky. |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
3rd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Pearson Education Limited, |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Harlow, England ; |
| -- |
New York : |
| Name of publisher, distributor, etc |
Pearson Education Limited, |
| Date of publication, distribution, etc |
2011. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
p. cm. |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
1: Artificial Intelligence --<br/>1: Introduction --<br/>1-1: What is AI? --<br/>1-2: Foundations of artificial intelligence --<br/>1-3: History of artificial intelligence --<br/>1-4: State of the art --<br/>1-5: Summary, bibliographical and historical notes, exercises --<br/>2: Intelligent agents --<br/>2-1: Agents and environments --<br/>2-2: Good behavior: the concepts of rationality --<br/>2-3: Nature of environments --<br/>2-4: Structure of agents --<br/>2-5: Summary, bibliographical and historical notes, exercises --<br/>2: Problem-Solving --<br/>3: Solving problems by searching --<br/>3-1: Problem-solving agents --<br/>3-2: Example problems --<br/>3-3: Searching for solutions --<br/>3-4: Uninformed search strategies --<br/>3-5: Informed (heuristic) search strategies --<br/>3-6: Heuristic functions --<br/>3-7: Summary, bibliographical and historical notes, exercises --<br/>4: Beyond classical search --<br/>4-1: Local search algorithms and optimization problems --<br/>4-2: Local search in continuous spaces --<br/>4-3: Searching with nondeterministic actions. 4-4: Searching with partial observations --<br/>4-5: Online search agents and unknown environments --<br/>4-6: Summary, bibliographical and historical notes, exercises --<br/>5: Adversarial search --<br/>5-1: Games --<br/>5-2: Optimal decisions in games --<br/>5-3: Alpha-beta pruning --<br/>5-4: Imperfect real-time decisions --<br/>5-5: Stochastic games --<br/>5-6: Partially observable games --<br/>5-7: State-of-the-art game programs --<br/>5-8: Alternative approaches --<br/>5-9: Summary, bibliographical and historical notes, exercises --<br/>6: Constraint satisfaction problems --<br/>6-1: Defining constraint satisfaction problems --<br/>6-2: Constraint propagation: inference in CSPs --<br/>6-3: Backtracking search for CSPs --<br/>6-4: Local search for CSPs --<br/>6-5: Structure of problems --<br/>6-6: Summary, bibliographical and historical notes, exercises --<br/>3: Knowledge. Reasoning And Planning --<br/>7: Logical agents --<br/>7-1: Knowledge-based agents --<br/>7-2: Wumpus world --<br/>7-3: Logic --<br/>7-4: Propositional logic: a very simple logic. 7-5: Propositional theorem proving --<br/>7-6: Effective propositional model checking --<br/>7-7: Agents based on propositional logic --<br/>7-8: Summary, bibliographical and historical notes, exercises --<br/>8: First-order logic --<br/>8-1: Representation revisited --<br/>8-2: Syntax and semantics of first-order logic --<br/>8-3: Using first-order logic --<br/>8-4: Knowledge engineering in first-order logic --<br/>8-5: Summary, bibliographical and historical notes, exercises --<br/>9: Inference in first-order logic --<br/>9-1: Propositional vs first-order inference --<br/>9-2: Unification and lifting --<br/>9-3: Forward chaining --<br/>9-4: Backward chaining --<br/>9-5: Resolution --<br/>9-6: Summary, bibliographical and historical notes, exercises -- |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
10: Classical planning --<br/>10-1: Definition of classical planning --<br/>10-2: Algorithms for planning as state-space search --<br/>10-3: Planning graphs --<br/>10-4: Other classical planning approaches --<br/>10-5: Analysis of planning approaches --<br/>10-6: Summary, bibliographical and historical notes, exercises. 11: Planning and acting in the real world --<br/>11-1: Time, schedules, and resources --<br/>11-2: Hierarchical planning --<br/>11-3: Planning and acting in nondeterministic domains --<br/>11-4: Multiagent planning --<br/>11-5: Summary, bibliographical and historical notes, exercises --<br/>12: Knowledge representation --<br/>12-1: Ontological engineering --<br/>12-2: Categories and objects --<br/>12-3: Events --<br/>12-4: Mental events and mental objects --<br/>12-5: Reasoning systems for categories --<br/>12-6: Reasoning with default information --<br/>12-7: Internet shopping world --<br/>12-8: Summary, bibliographical and historical notes, exercises --<br/>4: Uncertain Knowledge And Reasoning --<br/>13: Quantifying uncertainty --<br/>13-1: Acting under uncertainty --<br/>13-2: Basic probability notation --<br/>13-3: Inference using full joint distributions --<br/>13-4: Independence --<br/>13-5: Bayes' rule and its use --<br/>13-6: Wumpus world revisited --<br/>13-7: Summary, bibliographical and historical notes, exercises --<br/>14: Probabilistic reasoning. 14-1: Representing knowledge in an uncertain domain --<br/>14-2: Semantics of Bayesian networks --<br/>14-3: Efficient representation of conditional distributions --<br/>14-4: Exact inference in Bayesian networks --<br/>14-5: Approximate inference in Bayesian networks --<br/>14-6: Relational and first-order probability models --<br/>14-7: Other approaches to uncertain reasoning --<br/>14-8: Summary, bibliographical and historical notes, exercises --<br/>15: Probabilistic reasoning over time --<br/>15-1: Time and uncertainty --<br/>15-2: Inference in temporal models --<br/>15-3: Hidden Markov models --<br/>15-4: Kalman filters --<br/>15-5: Dynamic Bayesian Networks --<br/>15-6: Keeping track of many objects --<br/>15-7: Summary, bibliographical and historical notes, exercises -- |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
16: Making simple decisions --<br/>16-1: Combining beliefs and desires under uncertainty --<br/>16-2: Basis of utility theory --<br/>16-3: Utility functions --<br/>16-4: Multiattribute utility functions --<br/>16-5: Decision networks --<br/>16-6: Value of information. 16-7: Decision-theoretic expert systems --<br/>16-8: Summary, bibliographical and historical notes, exercises --<br/>17: Making complex decisions --<br/>17-1: Sequential decision problems --<br/>17-2: Value iteration --<br/>17-3: Policy iteration --<br/>17-4: Partially observable MDPs --<br/>17-5: Decisions with multiple agents: game theory --<br/>17-6: Mechanism design --<br/>17-7: Summary, bibliographical and historical notes, exercises. Learning --<br/>18: Learning from examples --<br/>18-1: Forms of learning --<br/>18-2: Supervised learning --<br/>18-3: Learning decision trees --<br/>18-4: Evaluating and choosing the best hypothesis --<br/>18-5: Theory of learning --<br/>18-6: Regression and classification with linear models --<br/>18-7: Artificial neural networks --<br/>18-8: Nonparametric models --<br/>18-9: Support vector machines --<br/>18-10: Ensemble learning --<br/>18-11: Practical machine learning --<br/>18-12: Summary, bibliographical and historical notes, exercises --<br/>19: Knowledge in learning --<br/>19-1: Logical formulation of learning --<br/>19-2: Knowledge in learning --<br/>19-3: Explanation-based learning --<br/>19-4: Learning using relevance information --<br/>19-5: Inductive logic programming --<br/>19-6: Summary, bibliographical and historical notes, exercises --<br/>20: Learning probabilistic models --<br/>20-1: Statistical learning --<br/>20-2: Learning with complete data --<br/>20-3: Learning with hidden variables: the EM algorithm. 20-4: Summary, bibliographical and historical notes, exercises --<br/>21: Reinforcement learning --<br/>21-1: Introduction --<br/>21-2: Passive reinforcement learning --<br/>21-3: Active reinforcement learning --<br/>21-4: Generalization in reinforcement learning --<br/>21-5: Policy search --<br/>21-6: Applications of reinforcement learning --<br/>21-7: Summary, bibliographical and historical notes, exercises --<br/>6: Communicating, Perceiving, And Acting --<br/>22: Natural language processing --<br/>22-1: Language models --<br/>22-2: Text classification --<br/>22-3: Information retrieval --<br/>22-4: Information extraction --<br/>22-5: Summary, bibliographical and historical notes, exercises -- |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
23: Natural language for communication --<br/>23-1: Phrase structure grammars --<br/>23-2: Syntactic analysis (parsing) --<br/>23-3: Augmented grammars and semantic interpretation --<br/>23-4: Machine translation --<br/>23-5: Speech recognition --<br/>23-6: Summary, bibliographical and historical notes, exercises --<br/>24: Perception --<br/>24-1: Image formation. 24-2: Early image-processing operations --<br/>24-3: Object recognition by appearance --<br/>24-4: Reconstructing the 3D world --<br/>24-5: Object recognition form structural information --<br/>24-6: Using vision --<br/>24-7: Summary, bibliographical and historical notes, exercises --<br/>25: Robotics --<br/>25-1: Introduction --<br/>25-2: Robot hardware --<br/>25-3: Robotic perception --<br/>25-4: Planning to move --<br/>25-5: Planning uncertain movements --<br/>25-6: Moving --<br/>25-7: Robotic software architectures --<br/>25-8: Application domains --<br/>25-9: Summary, bibliographical and historical notes, exercises --<br/>7: Conclusions --<br/>26: Philosophical foundations --<br/>26-1: Weak AI: can machines act intelligently? --<br/>26-2: Strong AI: can machines really think? --<br/>26-3: Ethics and risks of developing artificial intelligence --<br/>26-4: Summary, bibliographical and historical notes, exercises --<br/>27: AI: Present and future --<br/>27-1: Agent components --<br/>27-2: Agent architectures --<br/>27-3: Are we going in the right direction? 27-4: What if AI does succeed? --<br/>A: Mathematical background --<br/>A-1: Complexity analysis and O() notation --<br/>A-2: Vectors, matrices, and linear algebra --<br/>A-3: Probability distributions --<br/>B: Notes on languages and algorithms --<br/>B-1: Defining languages with Backus-Naur form (BNF) --<br/>B-2: Describing algorithms with pseudocode --<br/>B-3: Online help --<br/>Bibliography --<br/>Index. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Expert systems (Computer science) |
| 9 (RLIN) |
33121 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Artificial intelligence. |
| 9 (RLIN) |
33122 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
Books |
| Source of classification or shelving scheme |
Dewey Decimal Classification |