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TSP Greedy Methods
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Classroom Contents
Artificial Intelligence
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- 1 Artificial Intelligence: Introduction
- 2 Introduction to AI
- 3 AI Introduction: Philosophy
- 4 AI Introduction
- 5 Introduction: Philosophy
- 6 State Space Search - Introduction
- 7 Search - DFS and BFS
- 8 Search DFID
- 9 Heuristic Search
- 10 Hill climbing
- 11 Solution Space Search,Beam Search
- 12 TSP Greedy Methods
- 13 Tabu Search
- 14 Optimization - I (Simulated Annealing)
- 15 Optimization II (Genetic Algorithms)
- 16 Population based methods for Optimization
- 17 Population Based Methods II
- 18 Branch and Bound, Dijkstra's Algorithm
- 19 A* Algorithm
- 20 Admissibility of A*
- 21 A* Monotone Property, Iterative Deeping A*
- 22 Recursive Best First Search, Sequence Allignment
- 23 Pruning the Open and Closed lists
- 24 Problem Decomposition with Goal Trees
- 25 AO* Algorithm
- 26 Game Playing
- 27 Game Playing- Minimax Search
- 28 Game Playing - AlphaBeta
- 29 Game Playing-SSS *
- 30 Rule Based Systems
- 31 Inference Engines
- 32 Rete Algorithm
- 33 Planning
- 34 Planning FSSP, BSSP
- 35 Goal Stack Planning Sussman's Anomaly
- 36 Non-linear planning
- 37 Plan Space Planning
- 38 GraphPlan
- 39 Mod-01 Lec-39 Constraint Satisfaction Problems
- 40 Mod-01 Lec-40 CSP Continued
- 41 Mod-01 Lec-41 Knowlege Based Systems
- 42 Mod-01 Lec-42 Knowledge Based Systems PL
- 43 Mod-01 Lec-43 Propositional Logic
- 44 Mod-01 Lec- 44 Resolution Refutation for PL
- 45 Mod-01 Lec-45 First Order Logic (FOL)
- 46 Mod-01 Lec-46 Reasoning in FOL
- 47 Mod-01 Lec-47 Backward Chaining
- 48 Mod-01 Lec-48 Resolution for FOL
