Artificial intelligence : a modern approach, Stuart J. Russell and Peter Norvig ; contributing writers, Ernest Davis [and others]

Resource Information

The instance Artificial intelligence : a modern approach, Stuart J. Russell and Peter Norvig ; contributing writers, Ernest Davis [and others] represents a material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.

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http://covers.openlibrary.org/b/isbn/9780132071482-M.jpg?default=false

Label

Artificial intelligence : a modern approach, Stuart J. Russell and Peter Norvig ; contributing writers, Ernest Davis [and others]

Title remainder

a modern approach

Statement of responsibility

Stuart J. Russell and Peter Norvig ; contributing writers, Ernest Davis [and others]

Instantiates

• Artificial intelligence : a modern approach

Publication

• Upper Saddle River, NJ, Prentice Hall, ©2010

Bibliography note

Includes bibliographical references (pages 1063-1093) and index

Carrier category

volume

Carrier category code

• nc

Carrier MARC source

rdacarrier

Content category

text

Content type code

• txt

Content type MARC source

rdacontent

Contents

• Artificial Intelligence: -- Introduction: -- What is AI? -- Foundations of artificial intelligence -- History of artificial intelligence -- State of the art -- Summary, bibliographical and historical notes, exercises -- Intelligent agents: -- Agents and environments -- Good behavior: concept of rationality -- Nature of environments -- Structure of agents -- Summary, bibliographical and historical notes, exercises -- Problem-Solving: -- Solving problems by searching: -- Problem-solving agents -- Example problems -- Searching for solutions -- Uniformed search strategies -- Informed (heuristic) search strategies -- Heuristic functions -- Summary, bibliographical and historical notes, exercises -- Beyond classical search: -- Local search algorithms and optimization problems -- Local search in continuous spaces -- Searching with nondeterministic actions -- Searching with partial observations -- Online search agents and unknown environments--
• Summary, bibliographical and historical notes, exercises -- Adversarial search: -- Games -- Optimal decisions in games -- Alpha-beta pruning -- Imperfect real-time decisions -- Stochastic games -- Partially observable games -- State-of-the-art game programs -- Alternative approaches -- Summary, bibliographical and historical notes, exercises -- Constraint satisfaction problems: -- Defining constraint satisfaction problems -- Constraint propagation: inference in CSPs -- Backtracking search for CSPs -- Local search for CSPs -- Structure of problems -- Summary, bibliographical and historical notes, exercises -- Knowledge, Reasoning, And Planning: -- Logical agents: -- Knowledge-based agents -- Wumpus world -- Logic -- Propositional logic: a very simple logic -- Propositional theorem proving -- Effective propositional model checking -- Agents based on propositional logic -- Summary, bibliographical and historical notes, exercises -- First-order logic: -- Representation revisited--
• Syntax and semantics of first-order logic -- Using first-order logic -- Knowledge engineering in first-order logic -- Summary, bibliographical and historical notes, exercises -- Inference in first-order logic: -- Propositional vs first-order inference -- Unification and lifting -- Forward chaining -- Backward chaining -- Resolution -- Summary, bibliographical and historical notes, exercises -- Classical planning: -- Definition of classical planning -- Algorithms for planning as state-space search -- Planning graphs -- Other classical planning approaches -- Analysis of planning approaches -- Summary, bibliographical and historical notes, exercises -- Planning and acting in the real world: -- Time, schedules, and resources -- Hierarchical planning -- Planning and acting in nondeterministic domains -- Multiagent planning -- Summary, bibliographical and historical notes, exercises -- Knowledge representation: -- Ontological engineering -- Categories and objects -- Events--
• Mental events and mental objects -- Reasoning systems for categories -- Reasoning with default information -- Internet shopping world -- Summary, bibliographical and historical notes, exercises
• Uncertain Knowledge And Reasoning: -- Quantifying uncertainty: -- Acting under uncertainty -- Basic probability notation -- Inference using full joint distributions -- Independence -- Bayes' rule and its use -- Wumpus world revisited -- Summary, bibliographical and historical notes, exercises -- Probabilistic reasoning: -- Representing knowledge in an uncertain domain -- Semantics of Bayesian networks -- Efficient representation of conditional distributions -- Exact inference in Bayesian networks -- Approximate inference in Bayesian networks -- Relational and first-order probability models -- Other approaches to uncertain reasoning -- Summary, bibliographical and historical notes, exercises -- Probabilistic reasoning over time: -- Time an uncertainty -- Inference in temporal models -- Hidden markov models -- Kalman filters -- Dynamic Bayesian networks -- Keeping track of many objects -- Summary, bibliographical and historical notes, exercises -- Making simple decisions:--
• Combining beliefs and desires under uncertainty -- Basis of utility theory -- Utility functions -- Multiattribute utility functions -- Decision networks -- Value of information -- Decision-theoretic expert systems -- Summary, bibliographical and historical notes, exercises -- Making complex decisions: -- Sequential decision problems -- Value iteration -- Policy iteration -- Partially observable MDPs -- Decisions with multiple agents: game theory -- Mechanism design -- Summary, bibliographical and historical notes, exercises -- Learning: -- Learning from examples: -- Forms of learning -- Supervised learning -- Learning decision trees -- Evaluating and choosing the best hypothesis -- Theory of learning -- Regression and classification with linear models -- Artificial neural networks -- Nonparametric models -- Support vector machines -- Ensemble learning -- Practical machine learning -- Summary, bibliographical and historical notes, exercises -- Knowledge in learning:--
• Logical formulation of learning -- Knowledge in learning -- Explanation-based learning -- Learning using relevance information -- Inductive logic programming -- Summary, bibliographical and historical notes, exercises -- Learning probabilistic models: -- Statistical learning -- Learning with complete data -- Learning with hidden variables: the EM algorithm -- Summary, bibliographical and historical notes, exercises -- Reinforcement learning: -- Introduction -- Passive reinforcement learning -- Active reinforcement learning -- Generalization in reinforcement learning -- Policy search -- Applications of reinforcement learning -- Summary, bibliographical and historical notes, exercises -- Communicating, Perceiving, And Acting: -- Natural language processing: -- Language models -- Text classification -- Information retrieval -- Information extraction -- Summary, bibliographical and historical notes, exercises -- Natural language for communication: -- Phrase structure grammars--
• Syntactic analysis (parsing) -- Augmented grammars and semantic interpretation -- Machine translation -- Speech recognition -- Summary, bibliographical and historical notes, exercises -- Perception: -- Image formation -- Early image-processing operations -- Object recognition by appearance -- Reconstructing the 3D world -- Object recognition for structural information -- Using vision -- Summary, bibliographical and historical notes, exercises -- Robotics: -- Introduction -- Robot hardware -- Robotic perception -- Planning to move -- Planning uncertain movements -- Moving -- Robotic software architectures -- Application domains -- Summary, bibliographical and historical notes, exercises -- Conclusions: -- Philosophical foundations: -- Weak AI: can machines act intelligently? -- Strong AI: can machines really think? -- Ethics and risks of developing artificial intelligence -- Summary, bibliographical and historical notes, exercises -- AI: the present and future: -- Agent components--
• Agent architectures -- Are we going in the right direction? -- What if AI does succeed? -- Mathematical background: -- Complexity analysis and O() notation -- Vectors, matrices, and linear algebra -- Probability distribution -- Notes on languages and algorithms: -- Defining languages with Backus-Naur Form (BNF) -- Describing algorithms with pseudocode -- Online help -- Bibliography -- Index

Dimensions

26 cm.

Edition

3rd ed.

Extent

xviii, 1132 pages

Isbn

9780132071482

Lccn

2011288031

Media category

unmediated

Media MARC source

rdamedia

Media type code

• n

Other physical details

illustrations

Record ID

99208473410501161

System control number

• (OCoLC)359890490
• (OCoLC)ocn359890490