Artificial Intelligence

Artificial Intelligence. Instructor: Prof. Deepak Khemani, Department of Computer Science and Engineering, IIT Madras. This course provides an introduction to artificial intelligence. Topics include Introduction: Overview and Historical Perspective, Turing test, Physical Symbol Systems and the scope of Symbolic AI, Agents; State Space Search: Depth First Search, Breadth First Search, DFID; Heuristic Search: Best First Search, Hill Climbing, Beam Search, Tabu Search; Randomized Search: Simulated Annealing, Genetic Algorithms, Ant Colony Optimization; Finding Optimal Paths: Branch and Bound, A*, IDA*, Divide and Conquer approaches, Beam Stack Search; Problem Decomposition: Goal Trees, AO*, Rule Based Systems, Rete Net; Game Playing: Minimax Algorithm, Alpha-Beta Algorithm, SSS*; Planning and Constraint Satisfaction: Domains, Forward and Backward Search, Goal Stack Planning, Plan Space Planning, Graphplan, Constraint Propagation; Logic and Inferences: Propositional Logic, First Order Logic, Soundness and Completeness, Forward and Backward chaining. (from


Lecture 01 - Artificial Intelligence: Introduction
Lecture 02 - History of Artificial Intelligence: Mechanical Aspects
Lecture 03 - History of Artificial Intelligence: Philosophical Aspects
Lecture 04 - History of Artificial Intelligence
Lecture 05 - Introduction to Artificial Intelligence: Philosophy
Lecture 06 - State Space Search: Introduction
Lecture 07 - Search: Depth First Search and Breadth First Search
Lecture 08 - Search: Depth First Iterative Deepening (DFID)
Lecture 09 - Heuristic Search
Lecture 10 - Hill Climbing
Lecture 11 - Solution Space Search, Beam Search
Lecture 12 - Travelling Salesman Problem (TSP) Greedy Methods
Lecture 13 - Tabu Search
Lecture 14 - Optimization I (Simulated Annealing)
Lecture 15 - Optimization II (Genetic Algorithms)
Lecture 16 - Population based Methods for Optimization
Lecture 17 - Population based Methods II
Lecture 18 - Branch and Bound, Dijkstra's Algorithm
Lecture 19 - A* Algorithm
Lecture 20 - Admissibility of A*
Lecture 21 - A* Monotone Property, Iterative Deepening A*
Lecture 22 - Recursive Best First Search, Sequence Alignment
Lecture 23 - Pruning the Open and Closed Lists
Lecture 24 - Problem Decomposition with Goal Trees
Lecture 25 - AO* Algorithm
Lecture 26 - Game Playing
Lecture 27 - Game Playing - Minimax Search
Lecture 28 - Game Playing - Alpha-Beta
Lecture 29 - Game Playing - SSS*
Lecture 30 - Rule Based Systems
Lecture 31 - Inference Engines
Lecture 32 - Rete Algorithm
Lecture 33 - Planning
Lecture 34 - Planning FSSP, BSSP
Lecture 35 - Goal Stack Planning, Sussman's Anomaly
Lecture 36 - Nonlinear Planning
Lecture 37 - Plan Space Planning
Lecture 38 - GraphPlan
Lecture 39 - Constraint Satisfaction Problems
Lecture 40 - Constraint Satisfaction Problems (cont.)
Lecture 41 - Knowledge Based Systems
Lecture 42 - Knowledge Based Systems, Propositional Logic
Lecture 43 - Propositional Logic
Lecture 44 - Resolution Refutation for Propositional Logic
Lecture 45 - First Order Logic (FOL)
Lecture 46 - Reasoning in First Order Logic (FOL)
Lecture 47 - Backward Chaining
Lecture 48 - Resolution for First Order Logic (FOL)

Artificial Intelligence
Instructor: Prof. Deepak Khemani, Department of Computer Science and Engineering, IIT Madras. This course discusses topics in artificial intelligence.