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Artificial Intelligence I: Basics and Games in Java
Introduction
1. Introduction (1:37)
2. What is AI good for (4:39)
Graph-Search Algorithms
4. Why to consider graph algorithms (1:34)
5. Breadth-first search introduction (9:30)
6. Breadt-first search implementation 1 (12:10)
7. Depth-first search introduction 1 (10:21)
8. Depth-first search implementation I - with stack 1 (11:23)
9. Depth-first search implementation II - with recursion (4:17)
10. Enhanced search algorithms introduction (3:57)
11. Iterative deepening depth-first search (IDDFS) 1 (10:10)
12. A search introduction (7:08)
Basic Search / Optimization Algorithms
13. Brute-force search introduction (4:21)
14. Brute-force search example (9:15)
15. Stochastic search introduction (4:27)
16. Stochastic search example (8:06)
17. Hill climbing introduction (3:30)
18. Hill climbing example (7:35)
Meta-Heuristic Optimization Methods
19. Heuristics VS meta-heuristics (7:34)
20. Tabu search introduction (9:47)
22. Simulated annealing introduction 1 (10:19)
23. Simulated annealing - function extremum I (3:47)
24. Simulated annealing - function extremum II 1 (10:48)
25. Simulated annealing - function extremum III (4:24)
26. Travelling salesman problem I - city (9:51)
27. Travelling salesman problem II - tour 1 (13:10)
28. Travelling salesman problem III - annealing algorithm 1 (10:17)
29. Travelling salesman problem IV - testing (4:29)
31. Genetic algorithms introduction - basics (4:25)
32. Genetic algorithms introduction - chromosomes (2:26)
33. Genetic algorithms introduction - crossover (3:33)
34. Genetic algorithms introduction - mutation (3:11)
35. Genetic algorithms introduction - the algorithm (3:16)
36. Genetic algorithm implementation I - individual (9:07)
37. Genetic algorithm implementation II - population (5:36)
38. Genetic algorithm implementation III - the algorithm (9:22)
39. Genetic algorithm implementation IV - testing (7:25)
40. Genetic algorithm implementation V - function optimum 1 (10:50)
42. Swarm intelligence intoduction (7:01)
43. Partical swarm optimization introduction I - basics (7:39)
44. Partical swarm optimization introduction II - the algorithm 1 (10:19)
45. Particle swarm optimization implementation I - particle 1 (10:25)
46. Particle swarm optimization implementation II - initialize (7:13)
47. Particle swarm optimization implementation III - the algorithm 1 (10:08)
48. Particle swarm optimization implementation IV - testing (4:05)
Minimax Algorithm - Game Engines
49. Game trees introduction (4:13)
50. Minimax algorithm introduction - basics (4:15)
52. Minimax algorithm introduction - relation with tic-tac-toe (4:35)
53. Alpha-beta pruning introduction (5:04)
54. Alpha-beta pruning example (8:27)
55. Chess problem (2:11)
51. Minimax algorithm introduction - the algorithm (7:03)
Tic-Tac-Toe Game
56. About the game (3:11)
57. Cell (3:32)
58. Constants and Player (3:04)
59. Game implementation I (8:18)
60. Game implementation II (3:45)
61. Board implementation I (6:57)
62. Board implementationj II - isWinning() (3:53)
63. Board implementation III (6:19)
64. Minimax algorithm (9:07)
65. Running tic-tac-toe (4:37)
33. Genetic algorithms introduction - crossover
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