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Core/Elective: Core Semester: 1 Credits: 4
Course Description

Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course basics of modern AI as well as some of the representative applications of AI along with huge possibilities in the field of AI, which continues to expand human capability beyond our imagination are taught.

Course Objectives

To introduce the foundational principles of AI that drive real world complex applications and practice implementing some of these systems
To equip students with the tools to tackle new AI problems that they may encounter in life

Course Content

Module I
Introduction: Overview and Historical Perspective-Intelligent Agents-Problem Solving by searching-State Space Search: Depth First Search, Breadth First Search, DFID-Informed search & exploration-Heuristic Search-Best First Search-Hill Climbing-Beam Search-Tabu Search-Randomized Search:Simulated Annealing, Genetic Algorithms-Constraint Satisfaction Problems

Module II
Finding Optimal Paths: Branch and Bound, A*, IDA*, Divide and Conquer approaches-Beam Stack Search-Problem Decomposition: Goal Trees, AO*, Rule Based Systems -Game Playing: Minimax Algorithm, Alpha-Beta Algorithm, SSS*

Module III
Knowledge and reasoning: Propositional Logic-First Order Logic-Soundness and Completeness -Forward and Backward chaining-Resolution-semantic networks-Handling uncertain knowledge– Probabilistic Reasoning –making simple and complex decisions

Module IV
Planning : Planning problems -Planning with state space search -Partial order planning -Planning Graphs –Planning with Propositional logic-Hierarchical planning -Multi agent planning

Module V
Learning: Forms of learning-Inductive learning -Learning decision trees -Explanation based learning -Statistical learning -Instantance based learning –Neural networks-Reinforcement learning


1. Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach, 3e, Prentice Hall, 2009
2. Deepak Khemani. A First Course in Artificial Intelligence, 1e, McGraw Hill Education, 2017
3. Stefan Edelkamp and Stefan Schroedl. Heuristic Search: Theory and Applications, 1e, Morgan Kaufmann, 2011
4. Zbigniew Michalewicz and David B. Fogel. How to Solve It: Modern Heuristics, Springer; 2e, 2004
5. Elaine Rich and Kevin Knight. Artificial Intelligence, 3e, Tata McGraw Hill, 2017
6. Patrick Henry Winston. Artificial Intelligence, 1e, Pearson, 2002

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