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Artificial intelligence : a modern approach

Author: Stuart J Russell; Peter Norvig; Ernest Davis
Publisher: Upper Saddle River, NJ : Prentice Hall, ©2010.
Series: Prentice Hall series in artificial intelligence.
Edition/Format:   Book : English : 3rd edView all editions and formats
Database:WorldCat
Summary:
In this third edition, the authors have updated the treatment of all major areas. A new organizing principle--the representational dimension of atomic, factored, and structured models--has been added. Significant new material has been provided in areas such as partially observable search, contingency planning, hierarchical planning, relational and first-order probability models, regularization and loss functions in  Read more...
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Document Type: Book
All Authors / Contributors: Stuart J Russell; Peter Norvig; Ernest Davis
ISBN: 9780136042594 0136042597 9780132071482 0132071487
OCLC Number: 359890490
Description: xviii, 1132 pages : illustrations ; 26 cm.
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.
Series Title: Prentice Hall series in artificial intelligence.
Responsibility: Stuart J. Russell and Peter Norvig ; contributing writers, Ernest Davis [and others].

Abstract:

For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date  Read more...

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