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19-475-0102 ARTIFICIAL INTELLIGENCE
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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.
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| 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
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| 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
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| REFERNCES |
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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