Week 4

This week, we learned about adversarial search and we learned how it is used in games such as chess, tic-tac-toe, backgammon, and etc, to calculate the optimal move. These games are classified by how much information the agent can access in the game(perfect and imperfect information) and how the game is played which is either by chance like using dice or following a strict set of rules(deterministic and chance moves).

We learned about the minimax algorithm in which the computer assumes that the opponent will choose the minimizing move and choose a move considering both its move and the opponent’s optimal move. We also learned about alpha-beta pruning which is a search algorithm that aims to decrease the number of nodes that are evaluated by the minimax algorithm in its search tree. It stops evaluating a move when at least one possibility has been found that proves the move to be worse than a previously examined move.

We also did exercises about minimax and alpha-beta pruning to deepen our understanding of these algorithms.