Главная
Study mode:
on
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
Description:
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. Read more

Artificial Intelligence

NPTEL
Add to list
#Computer Science #Artificial Intelligence #Genetic Algorithms #Algorithms #Optimization Algorithms #Simulated Annealing #Search Algorithms #Constraint Satisfaction Problems
0:00 / 0:00
1 of 48

Artificial Intelligence: Introduction