Gupta Neeru, Mangla Ramita. Artificial Intelligence Basics: A Self-Teaching Introduction

Mercury Learning & Information, 2020. — 214 p. — ISBN 978-1-68392-516-3, 1683925165.Designed as a self-teaching introduction to the fundamental concepts of artificial intelligence, the book begins with its history, the Turing test, and early applications. Later chapters cover the basics of searching, game playing, and knowledge representation. Expert systems and machine learning are covered in detail, followed by separate programming chapters on Prolog and Python. The concluding chapter on artificial intelligence machines and robotics is comprehensive with numerous modern applications.Features.Covers an introduction to concepts related to AI, including searching processes, knowledge representation, machine learning, expert systems, programming, and robotics.
Includes separate chapters on Prolog and Python to introduce basic programming techniques in AI.Artificial intelligence (AI).
Computerized Reasoning.
Turing Test.
What is Intelligence?
Artificial Intelligence.
Goals of Artificial Intelligence.
History of Artificial Intelligence.
Advantages of Artificial Intelligence.
Application Areas of Artificial Intelligence.
Components of Artificial Intelligence.
Problem representation.
Introduction.
Problem Characteristics.
Problem Representation in AI.
Production System.
Conflict Resolution.
The search process.
Search Process.
Strategies for Search.
Search Techniques.
Game playing.
Game Playing.
Game Tree.
Components of a Game Playing Program.
Game Playing Strategies.
Problems in Computer Game Playing Programs.
Knowledge representation.
Introduction.
Definition of Knowledge.
Importance of Knowledge.
Knowledge-Based Systems.
Differences Between Knowledge-Based Systems and Database Systems.
Knowledge Representation Scheme.
Expert systems.
Introduction.
Definition of an Expert System.
Characteristics of an Expert System.
Architectures of Expert Systems.
Expert System Life Cycle.
Knowledge Engineering Process.
Knowledge Acquisition.
Difficulties in Knowledge Acquisition.
Knowledge Acquisition Strategies.
Advantages of Expert Systems.
Limitations of Expert Systems.
Examples of Expert Systems.
Learning.
Learning.
General Model for Machine Learning Systems.
Characteristics of Machine Learning.
Types of Learning.
Advantages of Machine Learning.
Disadvantages of Machine Learning.
Prolog.
Preliminaries of Prolog.
Milestones in Prolog Language Development.
What is a Horn Clause?
Robinson’s Resolution Rule.
Parts of a Prolog Program.
Queries to a Database.
How Does Prolog Solve a Query?
Compound Queries.
The_Variable.
Recursion in Prolog.
Data Structures in Prolog.
Head and Tail of a List.
Print all the Members of the List.
Print the List in Reverse Order.
Appending a List.
Find Whether the Given Item is a Member of the List.
Finding the Length of the List.
Controlling Execution in Prolog.
About Turbo Prolog.
Python.
Languages Used for Building AI.
Why Do People Choose Python?
Build AI Using Python.
Running Python.
Pitfalls.
Features of Python.
Useful Libraries.
Utilities.
Testing Code.
Artificial intelligence machines and robotics.
Introduction.
History: Serving, Emulating, Enhancing, and Replacing Man.
Technical Issues.
Applications: Robotics in the Twenty-First Century.
Summary.
Review questions.