Tutorial

 Artificial Intelligence

Unit-I    Introduction Lecture

Introduction: What is AI,

Foundations of AI, 

The State of Art. - Emerging Trends in Artificial Intelligence 

Intelligent Agent  Agents 

Environments, The Nature of Environments, 

The Structure of Agents.

Application of Artificial Intelligence

UNIT II    Solving Problems by searching Lecture                                                     

Problem Solving Agent

Problem Formulation

Example Problems - Searching for Solutions

Toy Problems 

Vacuum World Problem

8 Puzzle Problem

4 and 8 Queens Problems

Missionaries and Cannibals Problem

Uninformed Search Strategies, 

Breadth First Search

Depth first Search

Uniform Cost Search

Depth Limited Search

Iterative Deepening Search

Bidirectional Search

Informed search strategies, 

Greedy Best First Search

A* Search

Weighted A* Search

Beam Search 

Knowledge Based Agent

Wumpus World Problem

Beyond Classical Search: 

Local Search Algorithms 

Hill Climbing Algorithm

Simulated Annealing Algorithm

Genetic Algorithm

Local Search Algorithms and Optimization Problems, 

Local Search in Continues Spaces, 

Searching with Nondeterministic Actions, 

Searching with partial observations, 

online search agents and unknown environments.

Logical Reasoning

Propositional Logic Part-1 & Part-2 A Simple Knowledge Base

Inference in Propositional Logic

First order Logic

Semantic of First Order Logic

Knowledge Engineering in First order Logic

Inference in First Order Logic

Unification 

Unit-III - Reinforcement Learning

Reinforcement Learning 

Passive Reinforcement Learning, 

Active Reinforcement Learning, 

Natural Language Processing: Language Models, 

Text Classification, 

Information Retrieval, 

Information Extraction

UNIT IV                                Natural Language for Communication Lecture

Natural Language for Communication: Phrase Structure Grammar

 Syntactic Analysis, 

Augmented Grammars and 

SemanticInterpretation, 

Machine Translation, 

Speech Recognition

Perception: Image Formation, 

Early Image Processing Operations, 

Object Recognition by appearance, 

Reconstructing the 3D World, 

Object Recognition from Structural information, 

Using Vision.

UNIT V                                  Robotics Lecture                                                                                         

Robotics: Introduction, 

RobotHardware, 

Robotic Perception, 

planning to move, 

planning uncertain movements, 

Moving, Robotic software architectures, 

application domains

Philosophical foundations: Weak AI, Strong AI, Ethics and Risks of AI, Agent Components, Agent Architectures, Are we going in the right direction, What if AI does succeed.

Machine Learning