Download Artificial Intelligence A Modern Approach Third Edition By Stuart J. Russell And Peter Norvig

Introduction

We call ourselves Homo sapiens man the wise because our intelligence is so important to us. For thousands of years, we have tried to understand how we think; that is, how a mere handful of matter can perceive, understand, predict, and manipulate a world far larger and more complicated than itself. The field of artificial intelligence, or AI, goes further still: it ARTIFICIAL INTELLIGENCE attempts not just to understand but also to build intelligent entities. AI is one of the newest fields in science and engineering. Work started in earnest soon after World War II, and the name itself was coined in 1956. Along with molecular biology, AI is regularly cited as the “field I would most like to be in” by scientists in other disciplines. A student in physics might reasonably feel that all the good ideas have already been taken by Galileo, Newton, Einstein, and the rest. AI, on the other hand, still has openings for several full-time Einsteins and Edisons. AI currently encompasses a huge variety of subfields, ranging from the general (learning and perception) to the specific, such as playing chess, proving mathematical theorems, writing poetry, driving a car on a crowded street, and diagnosing diseases. AI is relevant to any intellectual task; it is truly a universal field.

Table Of Content

I Artificial Intelligence

2 Intelligent Agents

II Problem-solving

3 Solving Problems by Searching

4 Beyond Classical Search

5 Adversarial Search

6 Constraint Satisfaction Problems

III Knowledge, reasoning, and planning

7 Logical Agents

8 First-Order Logic

9 Inference in First-Order Logic

10 Classical Planning

11 Planning and Acting in the Real World

12 Knowledge Representation

IV Uncertain knowledge and reasoning

13 Quantifying Uncertainty

14 Probabilistic Reasoning

15 Probabilistic Reasoning over Time

16 Making Simple Decisions

17 Making Complex Decisions

V Learning

18 Learning from Examples

19 Knowledge in Learning

20 Learning Probabilistic Models

21 Reinforcement Learning\

VI Communicating, perceiving, and acting

22 Natural Language Processing

23 Natural Language for Communication

24 Perception

25 Robotics

VII Conclusions

26 Philosophical Foundations

27 AI: The Present and Future

 

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Introduction The current book is an outgrowth

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