ENGLISH

Artificial Intelligence Basics: A Self-Teaching Introduction

By Ph.D. Gupta , Neeru , Ramita Mangla

Book information

Publisher
Mercury Learning & Information
Year
2020
ISBN
1683925165, 9781683925163
Language
english
Format
PDF
Filesize
9 MB (9929915 bytes)
Pages
203\214
Time added
2021-03-02 18:05:48

Description

Designed as a self-teachingintroduction to the fundamental concepts of artificial intelligence, the book beginswith its history, the Turing test, and early applications. Later chapters coverthe basics of searching, game playing, and knowledge representation. Expertsystems and machine learning are covered in detail, followed by separateprogramming chapters on Prolog and Python. The concluding chapter on artificialintelligence machines and robotics is comprehensive with numerous modernapplications. Features: Covers an introduction toconcepts related to AI, including searching processes, knowledge representation,machine learning, expert systems, programming, and roboticsIncludes separate chapterson Prolog and Python to introduce basic programming techniques in AI Cover Half-Title page Title page Copyright page Contents Acknowledgments Chapter 1: ARTIFICIALINTELLIGENCE (AI) 1.1 Computerized Reasoning 1.2 Turing Test 1.3 What is Intelligence? 1.4 Artificial Intelligence 1.5 Goals of Artificial Intelligence 1.6 History of Artificial Intelligence 1.7 Advantages of Artificial Intelligence 1.8 Application Areas of Artificial Intelligence 1.9 Components of Artificial Intelligence Chapter 2: PROBLEM REPRESENTATION 2.1 Introduction 2.2 Problem Characteristics 2.3 Problem Representation in AI 2.4 Production System 2.5 Conflict Resolution Chapter 3: THE SEARCH PROCESS 3.1 Search Process 3.2 Strategies for Search 3.3 Search Techniques Chapter 4: GAME PLAYING 4.1 Game Playing 4.2 Game Tree 4.3 Components of a Game Playing Program 4.4 Game Playing Strategies 4.5 Problems in Computer Game Playing Programs Chapter 5: KNOWLEDGEREPRESENTATION 5.1 Introduction 5.2 Definition of Knowledge 5.3 Importance of Knowledge 5.4 Knowledge-Based Systems 5.5 Differences Between Knowledge-Based Systems andDatabase Systems 5.6 Knowledge Representation Scheme Chapter 6: EXPERT SYSTEMS 6.1 Introduction 6.2 Definition of an Expert System 6.3 Characteristics of an Expert System 6.4 Architectures of Expert Systems 6.5 Expert System Life Cycle 6.6 Knowledge Engineering Process 6.7 Knowledge Acquisition 6.8 Difficulties in Knowledge Acquisition 6.9 Knowledge Acquisition Strategies 6.10 Advantages of Expert Systems 6.11 Limitations of Expert Systems 6.12 Examples of Expert Systems Chapter 7: LEARNING 7.1 Learning 7.2 General Model for Machine Learning Systems 7.3 Characteristics of Machine Learning 7.4 Types of Learning 7.5 Advantages of Machine Learning 7.6 Disadvantages of Machine Learning Chapter 8: PROLOG 8.1 Preliminaries of Prolog 8.2 Milestones in Prolog Language Development 8.3 What is a Horn Clause? 8.4 Robinson’s Resolution Rule 8.5 Parts of a Prolog Program 8.6 Queries to a Database 8.7 How Does Prolog Solve a Query? 8.8 Compound Queries 8.9 The _ Variable 8.10 Recursion in Prolog 8.11 Data Structures in Prolog 8.12 Head and Tail of a List 8.13 Print all the Members of the List 8.14 Print the List in Reverse Order 8.15 Appending a List 8.16 Find Whether the Given Item is a Member of the List 8.17 Finding the Length of the List 8.18 Controlling Execution in Prolog 8.19 About Turbo Prolog Chapter 9: PYTHON 9.1 Languages Used for Building AI 9.2 Why Do People Choose Python? 9.3 Build AI Using Python 9.4 Running Python 9.5 Pitfalls 9.6 Features of Python 9.7 Useful Libraries 9.8 Utilities 9.9 Testing Code Chapter 10: ARTIFICIAL INTELLIGENCEMACHINES AND ROBOTICS 10.0 Introduction 10.1 History: Serving, Emulating, Enhancing, and Replacing Man 10.2 Technical Issues 10.3 Applications: Robotics in the Twenty-First Century 10.4 Summary REVIEW QUESTIONS Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 INDEX

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