Multimedia Blog

Week 1

For the first week in the intelligence class the class learned the introduction of the Intelligence System such as the History, Definition, and the basic premonition on how the AI logic work. The class also got the simple brief explanation of the final project and we also divide the teams. In the end i am one team with Nicholas Michael and Rino Santoso.

Week 2

For the second week in class in class i learn the action formulation of intelligent system where it is explained using the example of a ai vacuum cleaner. We also learned about the Uninformed search strategies where we review and learn Breadth first search, depth first search, depth limited, Uniform cost search, and Iterative deepening search. Afterward we do an exercise to demonstrate those algorithms in practice

Week 3

For the third week in class i learn the Local search algorithm that is Hill climbing that is a greedy algorithm that chooses solution based on what is best locally. I also learned the Local and Semi Global Search which is The Simulated Annealing where it uses temperature to find the optimum solution. Next i also learn the genetic algorithm that includes selection, crossover, mutation, and terminator that manipulates or modificate a problem to solve the problem. Afterward we do a practice on the genetic algorithm..

Week 4

In week 4 i learned about Adversial Search which is one of the algorithm that is used by an AI where it needs to compute solution when playing a game that consist of 2 more players. The adversial search algorithm that i learned that day is the Alpha Beta Pruning which is one of the Min Max algorithm. A min Max algorithm is an algorithm where the max is the value where it is the computer turn and the min is the value of the opponent turn. This min max algorithm is only used for Deterministic games which is games that has chances (There is no pattern which determine a clear win). At the end of the class i learn how to simulate the Alpha Beta Pruning min max algorithm