Learn How Wiley is Aiding the Global Community in Response to COVID-19



Artificial Intelligence, As per AICTE: Making a System Intelligent

Dr. Nilakshi Jain

ISBN: 9788126579945

444 pages

eBook also available for institutional users 

INR 499

Description



Artificial Intelligence: Making a System Intelligent explains concept of intelligent systems, techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation, and intelligent agents. The principles behind these techniques have been explained in this book without resorting to complex mathematics. The lack of assumed prior knowledge makes this book ideal for any introductory course in artificial intelligence or intelligent systems design. The contemporary coverage of this book is highly beneficial to advanced students as it facilitates in discovering state-of-the-art techniques, particularly in intelligent agents and knowledge discovery.


Appendix A


Appendix B


1 Introduction to Artificial Intelligence

1.1 Introduction

1.2 Definition of AI

1.3 Goals of AI

1.4 Abridged History and Foundation of AI

1.5 Branches or Subareas of AI

1.6 Applications of AI

1.7 Categorization of AI

1.8 Components of AI

1.9 Current Trends in AI

1.10 AI Programming Languages

 

2 Intelligent Agents

2.1 Introduction

2.2 Intelligent Systems

2.3 The Concept of Rationality

2.4 Types of Agents

2.5 Environments and Its Properties

2.6 PEAS Representation for an Agent

2.7 Intelligent Agent Application

 

3 Problem Solving

3.1 Introduction to Problem Solving

3.2 Problem Formulation

3.3 State–Space Representation

3.4 Problem Formulation of the Eight Tile Puzzle

3.5 Problem Formulation of Water Jug Problem

3.6 Problem Formulation Vacuum Cleaner World Problem

3.7 Problem Formulation of Wumpus World Problem

3.8 Problem Formulation of Missionaries and Carnivals Problem

3.9 Production System

3.10 Difference between Conventional Problems and AI Problems

3.11 Searching

3.12 Problem Characteristics and Issues in the Design of Search Programs

3.13 Solving Problems by Searching

3.14 Types of Search Strategies

 

4 Uninformed Search Strategies

4.1 Introduction

4.2 Brute Force or Blind Search

4.3 Breadth-First Search

4.4 Depth-First Search

4.5 Difference between BFS and DFS

4.6 Uniform Cost Search

4.7 Depth-Limited Search

4.8 Iterative Deeping DFS

4.9 Bidirectional Search

4.10 Comparing Uniform Search Strategies

 

5 Informed Search

5.1 Introduction

5.2 Hill Climbing

5.3 Best-First Search (Greedy Search)

5.4 A* Search

5.5 AO* Search: (AND–OR) Graph

5.6 Memory Bounded Heuristic Search

5.7 Simulated Annealing Search

5.8 Local Beam Search

5.9 Branch and Bound Search

 

6 Adversarial Search

6.1 Introduction

6.2 Optimal Strategies

6.3 The Minimax Algorithm

6.4 Alpha–Beta Pruning

 

7 Constraint Satisfaction Problem

7.1 Introduction

7.2 General Form of the CSP

7.3 Map Colouring Problem

7.4 N-Queens Problem

7.5 N-Queens Problem Formulation

7.6 Forward Checking

7.7 Crypto Arithmetic Problem

 

8 Knowledge and Reasoning

8.1 A Knowledge-Based Agent

8.2 The Wumpus World

8.3 Knowledge Representation Issues

 

9 Predicate Logic

9.1 Representation of Simple Fact in Logic

9.2 Representing Instance and Is_A Relationship in Predicate Logic

9.3 Computable Functions and Predicate Logic

9.4 Resolution

9.5 Knowledge Engineering in First-Order Logic

9.6 Unification

9.7 Natural Deduction

 

10 Representation Knowledge Using Rules

10.1 Propositional Logic

10.2 Frist-Order Logic/Predicate Logic

10.3 Inference in First-Order Logic

10.4 Procedural versus Declarative Knowledge

10.5 Logic Programming

10.6 Forward and Backward Reasoning

10.7 Matching

10.8 Control Knowledge

10.9 Forward and Backward Chaining (Type of Reasoning)

 

11 Planning and Learning

11.1 Introduction

11.2 The Language of Planning Problems

11.3 Planning with State Space Search

11.4 Partial Ordered Planning

11.5 Hierarchical Planning

11.6 Conditional Planning

11.7 Learning Introduction

11.8 Forms of Learning

11.9 Inductive Learning

11.10 Learning Decision Trees

11.11 Ensemble Learning

11.12 Reinforcement Learning

 

12 Uncertain Knowledge and Reasoning

12.1 Uncertainty

12.2 Basic Probability Theorem

12.3 Joint Probability

12.4 Baye’s Theorem

12.5 Representing Knowledge in an Uncertain Domain (Bayesian Belief Network)

12.6 Simple Inference in Belief Network

12.7 Temporal Model

12.8 Markov Decision Process

 

13 Natural Language Processing

13.1 Introduction

13.2 Exponential

13.3 Natural Language for Communication

13.4 Syntactic Analysis

13.5 Argumented Grammar

13.6 Semantic Interpretation

 

14 Expert System

14.1 Expert System

14.2 Need and Justification of ES

14.3 Knowledge Representation

14.4 Knowledge Acquisition and Variation

14.5 Utilisation and Functionality

14.6 Basics of Prolog

 

15 Application

15.1 Introduction

15.2 Category of Applications of AI

15.3 Robotics

15.4 Artificial Neural Network

15.5 AI Trends in Various Sectors

15.6 More About Agents of AI

 

16 Cognitive Computing

16.1 Introduction

16.2 Foundation of Cognitive Computing

16.3 List of Design Principles for Cognitive Systems

16.4 Natural Language Processing in Support of a Cognitive System

 

17 Introduction to Soft Computing and Fuzzy Logic

17.1 Introduction

17.2 Soft Computing versus Hard Computing

17.3 Various Types of Soft and Hard Computing Techniques

17.4 Fuzzy Logic

17.5 Fuzzy Set versus Crisp Set

17.6 Membership Function

17.7 Fuzzy Rules

17.8 Fuzzy Reasoning

17.9 Fuzzy Inference System

17.10 Fuzzification

17.11 Defuzzification

17.12 Fuzzy Controllers

 

18 Artificial Neural Network

18.1 Introduction to Artificial Neural Networks

18.2 Basic Models of Artificial Neural Networks

18.3 First Artificial Neurons: McCulloch–Pitts Model

18.4 Neural Network Architecture

18.5 Single-Layer Feedforward ANN

18.6 Multilayer Feedforward ANN

18.7 Activation Functions

18.8 Supervised Learning

18.9 Delta Learning Rule

18.10 Backpropagation Algorithm

18.11 Unsupervised Learning Algorithm

18.12 Self-Organising Maps

18.13 Hybrid Approach: Fuzzy Neural Systems

 

Summary

Review Questions

Short-Type Questions

Multiple-Choice Questions

Answers

 

Further Readings

Index