Artificial intelligence : a modern approach
Stuart J. Russell (Author), Ernest Davis, Peter Norvig (Author)
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 machine learning, kernel methods, Web search engines, information extraction, and learning in vision and robotics. The book also includes hundreds of new exercises
Print Book, English, 2015
Third edition; Indian edition View all formats and editions
Pearson India Education Services Pvt. Ltd., Noida, India, 2015
xviii, 1145 pages : illustrations ; 28 cm
9789332543515, 9332543518
928841872
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