Artificial Intelligence Basics: A Self-Teaching Introduction

✍ Scribed by Ph.D. Gupta, Neeru, Ramita Mangla

• Publisher: Mercury Learning & Information
• Year: 2020
• Tongue: English
• Leaves: 214
• Category: Library

✦ Synopsis

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 robotics

• Includes separate chapterson Prolog and Python to introduce basic programming techniques in AI

✦ Table of Contents

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