Knowledge Representation and Artificial Intelligence

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According to New Revised CBCS Syllabus w.e.f. 2020-21

A Text book of



Knowledge Representation and Artificial Intelligence

Author: Dr. Harshita Vachhani, Giridhar Gundre

Price: 310/-

ISBN: 978-93-90646-59-3

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1.    Introduction

1.    Introduction to Artificial Intelligence

2.    Importance of AI

3.    Role of AI in Daily Life Applications

4.    The History of AI

5.    What is Intelligence and Artificial Intelligence

6.    Different Task Domains of AI

7.    Programming Methods (Programming Without and With AI)

8.    Limitations of AI

9.    What is Intelligent Agent?

10.   Task Environment of Agents

11.   Classification of Agents

12.   Architecture of Agents 

2     Problem Solving

1.    Define Problems, Problem Spaces and Search: Define the Problem as a State Space Search, Problem Characteristics

1.1   Problems, Problem Spaces and Search

1.2   AI - General Problem Solving

1.3   Defining Problem as a State Space Search

2.    Production Systems

2.1   Problem Characteristics

2.2   Characteristics of Production Systems

3.    Uninformed Search Methods: Breadth First Search

3.1   Breadth First Search (BFS)

3.2   Depth-First Search

3.3   Bounded Depth-First Search

3.4   Depth Limited Search

3.5   Depth First Iterative Deepening (DFID)

4.    Informed Search Methods: Greedy best first Search, A* Search, Memory Bounded Heuristic Search

4.1   Best First Search Algorithm (Greedy Search)

4.2   A* Search Algorithm

4.3   Memory Bounded Heuristic Search

5.    Local Search Algorithms and Optimization Problems: Hill Climbing Search, Simulated Annealing, Local Beam Search, Genetic Algorithms

5.1   Hill Climbing Search

5.2   Simulated Annealing

5.3   Local Beam Search

5.4   Genetic Algorithms

6.    Adversarial Search: Games, Optimal Strategies, the Minimax Algorithm, Alpha-Beta Pruning

6.1   Game

6.2   Mini-Max Algorithm in Artificial Intelligence

6.3   Alpha-Beta Pruning 

3.    Knowledge Representation

1.    Knowledge Representation and Need of Knowledge Representation

2.    Knowledge Representation and Mapping Schemes

3.    Properties of Good Knowledge Based System

3.1   Types of Knowledge

4.    Knowledge Representation Issues

5.    Logic Representation

5.1   AND-OR Graph

6.    The Wumpus World, The Propositional logic

6.1   Exploring the Wumpus World

6.2   Knowledge-Base for Wumpus World

7.    First Order Logic: Syntax and Semantic, Inference in FOL

7.1   Knowledge Engineering in First-order Logic

8.    Forward and Backward Chaining 

4.    Propositional Logic

1.    Introduction

2.    Language

2.1   Syntax of Propositional Logic

2.2   Logical Connectives

2.3   Truth Table

3.    Logical (Semantic) Equivalence

4.    Rules of Inference and Natural Deduction

4.1   Inference

4.2   Types of Inference Rules

5.    Axiomatic Systems and Hibert Style Proofs

6.    The Tableau Method

7.    The Resolution Refutation Method 

5.    Uncertain Knowledge and Reasoning

1.    Uncertainty

2.    Representing Knowledge in an Uncertain Domain

2.1   Bayes' Theorem

3.    The Semantics of Belief Network

3.1   The Semantics of Bayesian Network

4.    Inference in Belief Network

4.1   Approximate Inferencing in Bayesian Networks 

6.    Planning

1.    Introduction to Planning

2.    A Simple Planning Agent

3.    Planning in State Space Search

4.    Various Planning Techniques

4.1   Partial Order Planning in AI

4.2   Hierarchical Planning in AI

4.3   Conditional Planning in AI 

7.    Learning

1.    Introduction to Machine Learning

2.    Classification of Machine Learning / Forms of Machine Learning

3.    Inductive Learning

4.    Decision Tree 

8.    Applications of AI

1.    Introduction

2.    Natural Language Processing (NLP)

3.    Expert Systems

3.1   Knowledge Base

3.2   Inference Engine

3.3   User Interface

3.4   Participants in the Development of Expert System

3.5   Why we should go for Expert System?

3.6   Steps for Development of Expert Systems

3.7   Capabilities of the Expert

3.8   Expert Systems Limitations

3.9   Applications of Expert System

4.    Artificial Neural Network

4.1   Bayesian Networks (BN)

5.    Case Study Based on Market Basket Analysis

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