Brenda's Blog

Student Journal

Brenda Spears – 2201841702

In the second week of the Intelligent Systems course, we were familiarized with uninformed search (DFS, BFS, etc.) .

In this lecture, we learned about the problem-solving agent, the goal or problem the agent tried to achieve or solve, the actions needed to be taken, and the knowledge the agent needs. The agent needs sufficient and adequate informations in order to reach the goal and able to deliver the descriptions of the situation.

Then, we learned the uniform search strategies. BFS using queue, DFS using stack, Depth-limited search (DFS with limited depth), Uniform-cost search using priority queue to order nodes, sorted by their path costs, IDS which requires modification to the tree search algorithm.

BFS properties:

– A finite branching factor makes it complete.

– All edges having the same cost makes it optimal.

– Take a lot of time and memory to find solutions with large number of steps.

DFS properties:

– May not terminate without a loop detection or depth bound.

– It will not be complete without a cycle detection.

– Does a chronological back tracking.

UCS properties:

– Branching factor is finite makes it complete.

For our group project, my team and I had come up with a decision to create a program that could detect facial emotions using tensorflow. We might integrate it, but still in the process of researching and finding other ideas.