# Artificial intelligence : a modern approach

, Print BookEnglish, 2015Edition: Third edition; Indian edition View all formats and editionsPublisher: Pearson India Education Services Pvt. Ltd., Noida, India, 2015

9789332543515, 9332543518

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