NPTEL : NOC:Artificial Intelligence Search Methods For Problem Solving (Computer Science and Engineering)

Co-ordinators : Prof. Deepak Khemani


Lecture 1 - Prologue

Lecture 2 - The Winograd Schema Challenge

Lecture 3 - Introduction (2013 version)

Lecture 4 - Can Machines Think?

Lecture 5 - The Turing Test

Lecture 6 - Language and Thought

Lecture 7 - The Willing Suspension of Disbelief

Lecture 8 - Machines with Wheels and Gears

Lecture 9 - The Notion of Mind in Philosophy

Lecture 10 - Reasoning = Computation

Lecture 11 - Concepts and Categories

Lecture 12 - How did AI get its name?

Lecture 13 - The Chess Saga

Lecture 14 - A Brief History of AI

Lecture 15 - The Worlds in our Minds

Lecture 16 - Epiphemona in Computers

Lecture 17 - State Space Search

Lecture 18 - Domain Independent Algorithms

Lecture 19 - Deterministic Search

Lecture 20 - DFS and BFS

Lecture 21 - Comparing DFS and BFS

Lecture 22 - Depth First Iterative Deepening

Lecture 23 - Heuristic Search

Lecture 24 - Heuristic Functions and the Search Landscape

Lecture 25 - Solution Space Search

Lecture 26 - The Traveling Salesman Problem

Lecture 27 - Escaping Local Optima

Lecture 28 - Stochastic Local Search

Lecture 29 - Genetic Algorithms: Survival of the Fittest

Lecture 30 - Genetic Algorithms and SAT

Lecture 31 - Genetic Algorithms for the TSP

Lecture 32 - Emergent Systems

Lecture 33 - Ant Colony Optimization

Lecture 34 - Finding Optimal Paths

Lecture 35 - Branch and Bound

Lecture 36 - Algorithm A*

Lecture 37 - A*: An illustrated example

Lecture 38 - Is A* Admissible?

Lecture 39 - Admissibility of A*

Lecture 40 - Higher, Faster ...

Lecture 41 - B&B - A* - wA* - Best First

Lecture 42 - A*: Leaner Admissible Variations

Lecture 43 - The Monotone Condition

Lecture 44 - DNA Sequence Alignment

Lecture 45 - Divide and Conquer Frontier Search.

Lecture 46 - Smart Memory Graph Search

Lecture 47 - Variations on A*: The story so far

Lecture 48 - Breadth First Heuristic Search

Lecture 49 - Beam Stack Search

Lecture 50 - Game Theory

Lecture 51 - Popular Recreational Games

Lecture 52 - Board Games and Game Trees

Lecture 53 - The Evaluation Function in Board Games

Lecture 54 - Algorithm Minimax and Alpha-Beta Pruning

Lecture 55 - A Cluster of Strategies

Lecture 56 - SSS*: A Best First Algorithm

Lecture 57 - SSS*: A Detailed Example

Lecture 58 - Automated Domain Independent Planning

Lecture 59 - The Blocks World Domain

Lecture 60 - State Space Planning: Forward and Backward

Lecture 61 - Goal Stack Planning (GSP)

Lecture 62 - GSP: A Detailed Example

Lecture 63 - Plan Space Planning (PSP)

Lecture 64 - PSP: A Tiny Example

Lecture 65 - Multi-Armed Robots

Lecture 66 - Means-Ends Analysis

Lecture 67 - The Planning Graph

Lecture 68 - Algorithm Graphplan

Lecture 69 - Problem Decomposition.

Lecture 70 - Algorithm AO*

Lecture 71 - AO*: An Illustration

Lecture 72 - Rule Based Expert Systems

Lecture 73 - The Inference Engine

Lecture 74 - The OPS5 Language

Lecture 75 - Conflict Resolution

Lecture 76 - Business Rule Management Systems

Lecture 77 - The Rete Net

Lecture 78 - Rete Algorithm: Optimizing the Match

Lecture 79 - Rete Algorithm: Conflict Resolution

Lecture 80 - Reasoning in Logic

Lecture 81 - Rules of Inference

Lecture 82 - Forward Reasoning

Lecture 83 - First Order Logic

Lecture 84 - Implicit Quantifier Notation

Lecture 85 - Backward Reasoning

Lecture 86 - Depth First Search on Goal Trees

Lecture 87 - Incompleteness...

Lecture 88 - Constraint Satisfaction Problems

Lecture 89 - Binary Constraint Networks

Lecture 90 - Interpreting Line Drawings

Lecture 91 - Model Based Diagnosis

Lecture 92 - Solving CSPs

Lecture 93 - Arc Consistency

Lecture 94 - Propagation = Reasoning

Lecture 95 - Lookahead Search