Thelong-anticipated revision of
ArtificialIntelligence: A Modern Approach
explores the full breadth and depth of the field of artificialintelligence (AI). The 4th Edition brings readers up to date on the latest technologies,presents concepts in a more unified manner, and offers new or expanded coverageof machine learning, deep learning, transfer learning, multi agent systems,robotics, natural language processing, causality, probabilistic programming,privacy, fairness, and safe AI.
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Maße
Höhe: 254 mm
Breite: 203 mm
Dicke: 62 mm
Gewicht
ISBN-13
978-1-292-40113-3 (9781292401133)
Schweitzer Klassifikation
Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California, Berkeley, where he is a Professor and former Chair of Computer Science, Director of the Centre for Human-Compatible AI, and holder of the SmithZadeh Chair in Engineering.
In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow. He held the Chaire Blaise Pascal in Paris from 2012 to 2014. He has published over 300 papers on a wide range of topics in artificial intelligence. His other books include: The Use of Knowledge in Analogy and Induction, Do the Right Thing: Studies in Limited Rationality (with Eric Wefald), and Human Compatible: Artificial Intelligence and the Problem of Control.
Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA's research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley.
He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and are research faculty member at Berkeley. His other books are: Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX.
The two authors shared the inaugural AAAI/EAAI Outstanding Educator award in 2016.
Chapter I Artificial IntelligenceIntroductionWhat Is AI?The Foundations of Artificial IntelligenceThe History of Artificial IntelligenceThe State of the ArtRisks and Benefits of AISummaryBibliographical and Historical NotesIntelligent AgentsAgents and EnvironmentsGood Behavior: The Concept of RationalityThe Nature of EnvironmentsThe Structure of AgentsSummaryBibliographical and Historical NotesChapter II Problem SolvingSolving Problems by SearchingProblem-Solving AgentsExample ProblemsSearch AlgorithmsUninformed Search StrategiesInformed (Heuristic) Search StrategiesHeuristic FunctionsSummaryBibliographical and Historical NotesSearch in Complex EnvironmentsLocal Search and Optimization ProblemsLocal Search in Continuous SpacesSearch with Nondeterministic ActionsSearch in Partially Observable EnvironmentsOnline Search Agents and Unknown EnvironmentsSummaryBibliographical and Historical NotesConstraint Satisfaction ProblemsDefining Constraint Satisfaction ProblemsConstraint Propagation: Inference in CSPsBacktracking Search for CSPsLocal Search for CSPsThe Structure of ProblemsSummaryBibliographical and Historical NotesAdversarial Search and GamesGame TheoryOptimal Decisions in GamesHeuristic Alpha--Beta Tree SearchMonte Carlo Tree SearchStochastic GamesPartially Observable GamesLimitations of Game Search AlgorithmsSummaryBibliographical and Historical NotesChapter III Knowledge, Reasoning and PlanningLogical AgentsKnowledge-Based AgentsThe Wumpus WorldLogicPropositional Logic: A Very Simple LogicPropositional Theorem ProvingEffective Propositional Model CheckingAgents Based on Propositional LogicSummaryBibliographical and Historical NotesFirst-Order LogicRepresentation RevisitedSyntax and Semantics of First-Order LogicUsing First-Order LogicKnowledge Engineering in First-Order LogicSummaryBibliographical and Historical NotesInference in First-Order LogicPropositional vs. First-Order InferenceUnification and First-Order InferenceForward ChainingBackward ChainingResolutionSummaryBibliographical and Historical NotesKnowledge RepresentationOntological EngineeringCategories and ObjectsEventsMental Objects and Modal Logicfor CategoriesReasoning with Default InformationSummaryBibliographical and Historical NotesAutomated PlanningDefinition of Classical PlanningAlgorithms for Classical PlanningHeuristics for PlanningHierarchical PlanningPlanning and Acting in Nondeterministic DomainsTime, Schedules, and ResourcesAnalysis of Planning ApproachesSummaryBibliographical and Historical NotesChapter IV Uncertain Knowledge and ReasoningQuantifying UncertaintyActing under UncertaintyBasic Probability NotationInference Using Full Joint DistributionsIndependence 12.5 Bayes' Rule and Its UseNaive Bayes ModelsThe Wumpus World RevisitedSummaryBibliographical and Historical NotesProbabilistic ReasoningRepresenting Knowledge in an Uncertain DomainThe Semantics of Bayesian NetworksExact Inference in Bayesian NetworksApproximate Inference for Bayesian NetworksCausal NetworksSummaryBibliographical and Historical NotesProbabilistic Reasoning over TimeTime and UncertaintyInference in Temporal ModelsHidden Markov ModelsKalman FiltersDynamic Bayesian NetworksSummaryBibliographical and Historical NotesMaking Simple DecisionsCombining Beliefs and Desires under UncertaintyThe Basis of Utility TheoryUtility FunctionsMultiattribute Utility FunctionsDecision NetworksThe Value of InformationUnknown PreferencesSummaryBibliographical and Historical NotesMaking Complex DecisionsSequential Decision ProblemsAlgorithms for MDPsBandit ProblemsPartially Observable MDPsAlgorithms for Solving POMDPsSummaryBibliographical and Historical NotesMultiagent Decision MakingProperties of Multiagent EnvironmentsNon-Cooperative Game TheoryCooperative Game TheoryMaking Collective DecisionsSummaryBibliographical and Historical NotesProbabilistic ProgrammingRelational Probability ModelsOpen-Universe Probability ModelsKeeping Track of a Complex WorldPrograms as Probability ModelsSummaryBibliographical and Historical NotesChapter V Machine LearningLearning from ExamplesForms of LeamingSupervised Learning .Learning Decision Trees .Model Selection and OptimizationThe Theory of LearningLinear Regression and ClassificationNonparametric ModelsEnsemble LearningDeveloping Machine Learning SystenSummaryBibliographical and Historical NotesKnowledge in LearningA Logical Formulation of LearningKnowledge in LearningExmplanation-Based LeaeningLearning Using Relevance InformationInductive Logic ProgrammingSummaryBibliographical and Historical NotesLearning Probabilistic ModelsStatistical LearningLearning with Complete DataLearning with Hidden Variables: The EM AlgorithmSummaryBibliographical and Historical NotesDeep LearningSimple Feedforward NetworksComputation Graphs for Deep LearningConvolutional NetworksLearning AlgorithmsGeneralizationRecurrent Neural NetworksUnsupervised Learning and Transfer LearningApplicationsSummaryBibliographical and Historical NotesReinforcement LearningLearning from RewardsPassive Reinforcement LearningActive Reinforcement LearningGeneralization in Reinforcement LearningPolicy SearchApprenticeship and Inverse Reinforcement LeamingApplications of Reinforcement LearningSummaryBibliographical and Historical NotesChapter VI Communicating, perceiving, and actingNatural Language ProcessingLanguage ModelsGrammarParsingAugmented GrammarsComplications of Real Natural LanguagrNatural Language TasksSummaryBibliographical and Historical NotesDeep Learning for Natural Language ProcessingWord EmbeddingsRecurrent Neural Networks for NLPSequence-to-Sequence ModelsThe Transformer ArchitecturePretraining and Transfer LearningState of the artSummaryBibliographical and Historical NotesRoboticsRobotsRobot HardwareWhat kind of problem is robotics solving?Robotic PerceptionPlanning and ControlPlanning Uncertain MovementsReinforcement Laming in RoboticsHumans and RobotsAlternative Robotic FrameworksApplication DomainsSummaryBibliographical and Historical NotesComputer VisionIntroductionImage FormationSimple Image FeaturesClassifying ImagesDetecting ObjectsThe 3D WorldUsing Computer VisionSummaryBibliographical and Historical NotesChapter VII ConclusionsPhilosophy, Ethics, and Safety of AlThe Limits of AlCan Machines Really Think?The Ethics of AlSummaryBibliographical and Historical NotesThe Future of AIAl ComponentsAl ArchitecturesA Mathematical BackgroundA.1 Complexity Analysis and O0 NotationA.2 Vectors, Matrices, and Linear AlgebraA.3 Probability DistributionsBibliographical and Historical Notes B Notes on Languages and AlgorithmsB.1 Defining Languages with Backus-Naur Form (BNF)B.2 Describing Algorithms with PseudocodeB.3 Online Supplemental Material BibliographyIndex
