Artificial Intelligence Essentials

AD25401:

Artificial Intelligence Essentials

Course Objectives:

This course aims to introduce the essential concepts of Artificial Intelligence, covering key areas such as search strategies, knowledge representation, planning, learning and communication.

Introduction: Artificial Intelligence (AI) – Applications and Future - Intelligent Agents: Agents and Environments – Characteristics - Task Environments & their properties – Types – Architecture – Problem solving approach to AI problems.

Activities

● Assignment: Impact of AI in the next 10 years.

● Quiz: Agents and Environments.

Search Strategies: Uninformed Search: Breadth First Search (BFS) – Depth First Search (DFS) - Depth-Limited Search (DLS) - Informed Search: Greedy Best First Search – A* Search – Iterative Deepening A* Search (IDA*). Local Search Algorithms and Optimization Problems: Hill Climbing - Simulated Annealing – Genetic Algorithms – Beam Search – Local Beam Search - Constraint Satisfaction Problems (CSPs) - Adversarial Search.

Activities:

● Project based learning:

○ Solve a CSP like Sudoku.

○ Develop a Tic-Tac-Toe AI.

Knowledge Representation in Logic

Propositional Logic Vs Predicate Logic - First Order Predicate Logic: Syntax - Semantics – Usage - Knowledge Representation. Inference in First Order Logic: Unification - Resolution – Forward Chaining - Backward Chaining - Simple Logic programming using Prolog for Unification, Resolution, Forward and Backward Chaining.

Activities:

● Review of GATE Questions.

● Assignment: Building a Simple Rule based expert system.

Semantic Modelling and Planning

Knowledge Representation: Ontological Engineering - Categories and Objects – Events - Mental Events and Mental Objects. Reasoning Systems for Categories - Reasoning with Default Information. Planning in AI: Problems – Algorithms – Forward State Space Search – Backward State Space Search – Heuristics – Graphs.

Activities:

● Flipped Class Room: For a given scenario, identify the different objects, events, categories and their relationships.

Quiz: Planning problems.

Learning and Communication

Learning in AI: Supervised Learning – Unsupervised Learning – Reinforcement Learning. Agent communication – Criteria for evaluation – Conceptual Foundations – Traditional Software Engineering Approaches – Agent Communication Languages (ACL) – Commitment Protocols – Modelling Communications.

Activities:

● Assignment: Real world applications of different learning models.

● Quiz: Agent Communication.

Weightage:

Continuous Assessment: 40%

 (i) .Activities: 30%

 (ii) .Internal Examinations: 70%

EndSemesterTheory Examination: 60%

Mandated Activities with marks:

Assignments (30), Quiz (10), Project based Learning (25), Flipped Classroom (10), Review of GATE & IES questions (25).

Internal Examinations: TWO tests

References:

1. S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach”, Pearson, Fourth Edition, 2020.

2. Gerhard Weiss, “Multiagent Systems”, Second Edition, MIT Press, 2016.

3. Chandramouli Subramanian and Asha A George, Artificial Intelligence: Principles & Application, Universities Press India Pvt. Ltd. 1st Edition, 2026.

4. Bratko, “Prolog: Programming for Artificial Intelligence”, Fourth edition, Addison Wesley Educational Publishers Inc., 2011.

5. M. Tim Jones, “Artificial Intelligence: A Systems Approach (Computer Science)”, Jones and Bartlett Learning, Second Edition, 2019.

6. Nils J. Nilsson, “The Quest for Artificial Intelligence”, Cambridge University Press, Second Edition, 2020.