Jharkhand University of Technology, Jharkhand, Computer Engineering Semester 7, Artificial Intelligence Syllabus

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Unit - 1 Introduction

Unit 1

Introduction

1.1 Overview of AI

1.2 Problems of AI

1.3 AI techniques

1.4 Problem Solving

1.5 Problem Space and Search

1.6 Defining the problem as state space search

1.7 Problem characteristics

1.8 Tic Tac Toe Problem

1.9 AI languages Basic knowledge of AI programming languages like Prolog and Lisp

Unit - 2 Basic Search Techniques

Unit 2

Basic Search Techniques

2.1 Solving Problems by searching Uniform search strategies Breadth first search depth first search depth limited search bidirectional search Best First search

2.2 Uninformed Search Strategies Breadthfirst search

2.4 Comparing search strategies in terms of complexity

Unit - 3 Special Search Techniques

Unit 3

Special Search Techniques

3.1 Heuristic search

3.2 Greedy best

3.3 Best First Search

3.4 A* Search Algorithm

3.5 Hill Climbing

3.6 Simulated Annealing

3.7 Genetic Algorithm

3.8 Constraint Satisfaction Problems

3.9 Adversarial Search

3.10 Games

3.11 Optimal decisions and strategies in games

3.12 Minimax search

3.13 Alpha Beta pruning

3.14 Symbolic Logic Syntax and semantics for propositional logic

3.15 Syntax and semantics of FOPL

3.16 Properties of WFF

3.17 Clausal form

3.18 Unification

3.19 Resolution

Unit - 4 Reasoning Under Inconsistencies and Uncertainties

Unit 4

Reasoning Under Inconsistencies and Uncertainties

4.1 Non monotonic reasoning

4.2 Truth Maintenance System

4.3 Default Reasoning closed world assumption

4.4 Predicate completion and circumscription

4.5 Fuzzy Logic

4.6 Probabilistic Reasoning Bayesian probabilistic inference

4.7 Representation of knowledge in uncertain domain

4.8 Semantics of Bayesian Networks

4.9 Dempster Shafer theory

Unit - 5 Structured Knowledge

Unit 5

Structured Knowledge

5.1 Structured Knowledge Associative networks

5.2 Conceptual graphs

5.3 Frames structures

5.4 Expert Systems Rule based systems

5.5 Non production systems decision tree architectures

5.6 Black board system architecture

5.7 Neural network architecture

5.8 Learning Types of learning

5.9 General learning model

5.10 Learning by induction

5.11 Generalization specialization

5.12 Example of inductive learner

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Other Subjects of Semester-1
Machine learning
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