IAI

Subject Name: Introduction to Artificial Intelligence (IAI)

Class: B.Tech. - IV year (ECE)

Semester: VII SEM

Academic Year : 2025 - 2026

Resource Materials:              Unit - 1                Unit - II

Syllabus

23AD81-INTRODUCTION TO ARTIFICIAL INTELLIGENCE 

Pre-requisite                               :       Basic Engineering Mathematics Knowledge

Course Educational Objective : The objective of the course is to present an overview of artificial intelligence (AI) principles and approaches. Develop a basic understanding of the building blocks of AI as presented in terms of intelligent agents: Search, Knowledge representation, inference, logic, reasoning, and learning. Students will implement a small AI system in a team environment. The knowledge of artificial intelligence plays a considerable role in some applications students develop for courses in the program.

Course Outcomes: At the end of this course, the student will be able to

CO1: Enumerate the history and foundations of Artificial Intelligence. (Understand-L2)

CO2: Apply the basic principles of AI in problem solving. (Apply-L3).

CO3: Illustrate the different searching algorithms to find and optimize the solution for the given problem. (Apply-L3)

CO4: Illustrate the different gaming algorithms and identify the importance of knowledge representation in Artificial Intelligence. (Apply- L3)

CO5: Describe the use of predicate logic to represent the knowledge in AI domain.

(Understand - L2)

 UNIT I

Introduction: What Is AI?, The Foundations of Artificial Intelligence, The History of Artificial Intelligence, The State of the Art, Agents and Environments, Good Behavior: The Concept of Rationality, The Nature of Environments, The Structure of Agents.

 UNIT II

Problem Solving: Problem-Solving Agents, Example Problems, searching for Solutions, search algorithms terminologies, properties of search algorithms, types of search algorithms. Informed and Uninformed Search Strategies, Informed (Heuristic) Search Algorithms: Best first search, A* Algorithm, AO* Algorithm and Local Search Algorithms. Searching with Non deterministic Actions.

 UNIT III

Search Algorithms: Uniformed / Blind Search Algorithms: Breadth- first Search, Depth- first Search, Depth-limited Search, Iterative deepening depth-first search, Uniform cost search, Bidirectional Search.

                  UNIT IV

Adversarial Search/ Game Playing: Introduction, Minimax algorithm, Alpha-Beta pruning. Knowledge-Based Agent: Architecture, Knowledge base Levels, Types , Knowledge Representation mappings, forward and backward chaining/Reasoning techniques of inference engine , Approaches of knowledge representation, issues in knowledge representation.

 UNIT V

Knowledge Representation Techniques: Propositional Logic: A Very Simple Logic, Ontological Engineering, Categories and Objects, Events, Mental Events and Mental Objects, What is Reasoning? Types of Reasoning and Reasoning Systems for Categories, The Internet Shopping World.

TEXTBOOKS:

1.        StuartRussell,PeterNorvig,“ArtificialIntelligence:AModernApproach”,3rdedition,Prentice Hall,2009.Canalsouse 2nd Ed.,PearsonEducationInternational,2003.

2.        Saroj Kaushik, “Artificial Intelligence”, Cengage Learning India,2011

3.        Rich & Knight, Artificial Intelligence, second edition,Tata Mc GrawHill.

 REFERENCE BOOKS:

1.       Nils Nilsson, “Artificial Intelligence: A New Synthesis”, Morgan Kaufmann, 1998.

2.       David Poole, Alan Mackworth, “Artificial Intelligence: Foundations for Computational Agents”, Cambridge Univ. Press, 2010.

3.       Ronald Brachman, “Knowledge Representation and Reasoning”, Morgan Kaufmann, 2004.

4.       Frank van Harmelen, Vladimir Lifschitz, Bruce Porter (Eds), “Handbookof Knowledge representation”, Elsevier, 2008.

5.       Ivan Bratko, “Prolog Programming for Artificial Intelligence”, 4th Ed., Addison-Wesley, 2011.