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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 nptel.ac.in)

Lecture 22 - Recursive Best First Search, Sequence Alignment

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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)