This week’s session is about uninformed search such as the breadth first search, depth first search, iterative deepening depth first search, uniform cost search and depth limited search.

First of all, for the problem solving, it needs to know about the situation. What is their goal and their environment. They need to know in which situation they have to respond and which they need to ignore. So knowledge about their surroundings have to be adequate. And, in their data, they should know about the state, the cost of the path, and the action in the path (right, left, up, down), and the number of steps (level of nodes) they should take.

Secondly, we learned about the difference of each search algorithm, each have it’s own advantages and weaknesses for the problem solving. As for breadth first search, it visit each node by their level. In depth first search, it visit node by their child and backtrack if their bottom child is not the goal and search for the other child. Uniform cost search, use priority queue, so it search the node by their smallest cost possible.

For the project, me and my group decided to make something about recognizing emotions using tensorflow. We might elaborate it into something not just an emotions recognition, however, we are still deciding and researching about it.