Artificial Intelligence: Concepts and Applications

Lavika Goel

ISBN: 9788126519934

780 pages

INR 779


Artificial Intelligence: Concepts and Applications is a comprehensive discourse on the fundamental principles and concepts that lead to building artificially intelligent programs. It details the wide range of possible application areas where artificial intelligence can be used. The concepts of heuristic search and development of meta-heuristic algorithms has led a far way towards the development of computational intelligence algorithms and nature inspired algorithms that have been used in a variety of problem  solving methods.




About the Author

List of Video Content


PART I Foundations of Artificial Intelligence

Chapter 1 Basics of Artificial Intelligence

1.1 What is Artificial Intelligence?

1.2 Definition of Artificial Intelligence Through Problems

1.3 History of Artificial Intelligence

1.4 Artificial Intelligence – Problems and Techniques

1.5 Production Systems

1.6 Shift in Focus of AI Towards Providing Smarter Solutions


Chapter 2 Problem Solving Methods in Artificial Intelligence

2.1 Introduction

2.2 State Space Search

2.3 Production System

2.4 Problem Characteristics

2.5 Control Strategy

2.6 Issues in the Design of Search Programs

2.7 Search Strategies

2.8 Advanced Problems


Chapter 3 Informed and Uninformed Search Strategies

3.1 Introduction

3.2 Generate-and-Test Method

3.3 Hill Climbing Method

3.4 Best First Search and A* Search

3.5 Means End Analysis

3.6 Intelligent Agents and Environment

3.7 Problem Reduction, AO* Algorithm

3.8 Constraint Satisfaction with Inference, Backtracking, and Local Search

3.9 Local Search Algorithms and Optimization Problems

3.10 Local Search in Continuous Spaces


Chapter 4 Knowledge Representation

4.1 Introduction

4.2 Ontologies, Objects, and Events

4.3 Representations and Mappings

4.4 Approaches to Knowledge Representation

4.5 Forward versus Backward Chaining

4.6 Matching and Control Knowledge

4.7 Slot and Filler Structures

4.8 Issues in Knowledge Representation

4.9 Developments in the Field of Knowledge Representation


PART II Basics of Machine Learning


Chapter 5 Neural Networks and Applications

5.1 Introduction

5.2 Learning in Neural Networks

5.3 Choosing Cost Function

5.4 Types of Learning

5.5 Recurrent Neural Network

5.6 Back-propagation

5.7 Convolutional Neural Networks and Deep Neural Networks

5.8 Applications of Neural Networks

5.9 Challenges in Neural Networks


Chapter 6 Fuzzy Logic and Applications

6.1 Introduction

6.2 Set Theory

6.3 Fuzzy Set Theory

6.4 Terminology Associated with Fuzzy Sets

6.5 Fuzzification and Defuzzification

6.6 Formation of Fuzzy Rules

6.7 Fuzzy Logic Inference System

6.8 Fuzzy Database and Queries

6.9 Fuzzy Logic Control System

6.10 Fuzzy Inference Processing: Mamdani and Sugeno

6.11 Adaptive Neuro-Fuzzy Inference System

6.12 Applications


Chapter 7 Statistical Machine Learning

7.1 Introduction

7.2 Probability Axioms

7.3 Bayes’ Rule

7.4 Bayesian Network

7.5 Dynamic Bayesian Networks

7.6 Hidden Markov Model

7.7 Probabilistic Reasoning

7.8 Certainty Factor Theory

7.9 Dempster–Shafer Theory


Chapter 8 Decision Processes and Reinforcement Learning

8.1 What is Learning?

8.2 Forms of Learning

8.3 Learning Decision Trees

8.4 Theory of Learning

8.5 Learning by Examples

8.6 Inductive Learning

8.7 Explanation-Based Learning

8.8 Regression and Classification with Linear Models

8.9 Artificial Neural Networks

8.10 Parametric Models

8.11 Non-Parametric Models

8.12 Support Vector Machines

8.13 Ensemble Learning

8.14 Statistical Learning

8.15 Reinforcement Learning

8.16 Applications of Reinforcement Learning


Chapter 9 Classification Problems in Machine Learning

9.1 Utility Theory

9.2 Multi-Attribute Utility Function

9.3 Decision Network

9.4 Value of Information

9.5 Decision-Theoretic Expert Systems

9.6 Sequential Decision Problems

9.7 Multiple Agent Solution: Game Theory

9.8 Mechanism Design

9.9 Modern Approaches to Classification


PART III Applications of Artificial Intelligence


Chapter 10 Game Playing

10.1 Introduction

10.2 Minimax Search Procedure

10.3 Alpha–Beta Cutoff

10.4 Imperfect Real-Time Decisions

10.5 Stochastic Games

10.6 State-of-the-Art Game Programs

10.7 Modern Examples


Chapter 11 Text Analysis and Mining

11.1 Introduction

11.2 Language Models

11.3 Text Classification

11.4 Information Retrieval

11.5 Information Extraction

11.6 Phrase Structure Grammar

11.7 Syntactic Processing

11.8 Augmented Grammars and Semantic Analysis

11.9 Discourse and Pragmatic Processing

11.10 Statistical Natural Language Processing

11.11 Cross-Lingual Natural Language Processing

11.12 Spell Checking

11.13 Speech Recognition

11.14 Use of Python’s NLTK Library in Modern Text Mining Applications

11.15 Case Study: Sentiment Analysis of User Comments on Social Networking Website Twitter using Machine Learning


Chapter 12 Expert Systems and Applications

12.1 Expert System

12.2 Knowledge Representation

12.3 Expert System Shells

12.4 Knowledge Acquisition of an Expert System

12.5 Applications of Expert Systems

12.6 Examples of Expert Systems

12.7 Problem Solving Examples


PART IV Logic in Artificial Intelligence


Chapter 13 First-Order Logic

13.1 Introduction

13.2 Propositional Logic

13.3 First-Order Logic


Chapter 14 Prolog

14.1 Introduction

14.2 Logic Programming: Symbolic Logic, Clausal Form

14.3 Converting English to Prolog Facts and Rules

14.4 Prolog Terminology

14.5 Variables and Arithmetic Operators

14.6 Inference Process of Prolog

14.7 Tracing Model of Execution

14.8 List Structures

14.9 Operations on List

14.10 Drawbacks of Prolog

14.11 Applications of Logic Programming


Chapter 15 Modern Artificial Intelligence Languages and Tools

15.1 Python


15.3 R


PART V Trends in Machine Learning


Chapter 16 Concepts in Machine Learning

16.1 Introduction

16.2 Approaches to Machine Learning

16.3 Building Efficient Machine Learning Systems

16.4 Reasons for Sudden Spurt in Use of Machine Learning

16.5 Artificial Intelligence versus Machine Learning

16.6 Taxonomy of Machine Learning Based Techniques

16.7 List of Machine Learning Softwares


Chapter 17 Advanced Topics in Machine Learning

17.1 Introduction

17.2 Artificial Immune System

17.3 Swarm Intelligence

17.4 Geoscience-Based Techniques

17.5 Selection of Suitable Technique Based on Problem Characteristics

17.6 Performance Validation of Intelligent Systems Using Statistics

17.7 Applied Machine Learning


Appendix A Project Work

Appendix B Multiple-Choice Questions and Answers

Appendix C Interview Questions and Answers

Appendix D Bibliography



Congratulations Dr. Lavika Goel author of Artificial Intelligence: Concepts and Applications for being declared as runner up under BEST WOMAN PROFESSIONAL (Early Career) category by IEEE India Council for Dr. Prahlad P. Chhabria award 2021.

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This is a good book for the young aspiring beginners as it bridges the gap between the Campus and Corporate.  With due respect to all the great authors on Artificial Intelligence in the past, I really envy that a book of this caliber was not available during my graduation days. It is a very well organized book which not only provides a fruitful reading experience to a beginner, but provides enough means through various levels of typical exercises with sufficient room for detailed learning opportunity. The author has contributed a wonderful resource material coupled with a range of programs using Prolog to Python and also unveiled her passion to visualize the forthcoming endeavours associated with the AI domain. I deem it a privilege to give my remarks on this well authored text book.

G. Praveen Babu

Associate Professor of CSE & Coordinator,University Academic Audit Cell

JNTUH, Kukatpally

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