This week we learned about Adversarial Search that is used in games to considers the opponent’s move when deciding the best possible move. We studied about the minimax algorithm which maximizes our chances of winning by taking account enemy’s best move. The algorithm works on deterministic and non-deterministic games.

On top of that, we also discussed about alpha and beta pruning which eliminates the branches based on the information we have on the other branch. The pruning is centered around the idea that the enemy could cut unnecessary steps that the player wouldn’t take. This is implemented using DFS to try the finding the best path towards the solution.

This week we studied the informed searches. In the informed searches, the functions possesses heuristic value alongside its path cost. In this session we learned and practiced on multiple algorithms such as A*, greedy, and genetic, which means that we continued learning into further detail of the A* algorithm after we have implemented the coding aspect of it in the second week during the lab class session.

The A* algorithm takes into consideration not only the heuristic value, but also its path cost unlike the greedy algorithm, which only considers the heuristic value. We also studied local search that includes hill climbing, which searches neighbors and check the most optimal value from the results. The genetic algorithm that we also practiced during this session is a trial and error method that occurs until the best solution is found.

In this week’s session we studied about uninformed search. Some aspect of the uninformed search strategy is familiar to us as the uninformed search consists of Breadth First Search and Depth First Search. However we didn’t only recall on BFS and DFS in this session as we also learned more on the uninformed search strategies which are (BFS and DFS) FLS, UCS, and IDS.

These uninformed search strategies are used in order to search for results. A state graph has vertices and edges, as each state has its own path cost where these uninformed search strategies try to come up with an optimal result to reach its goal. And in order to practice and test our understandings, we were given an exercise to implement the uninformed search strategies that we learned in class.