Lets walk through the uninformed search example question(s)
Consider the above map of cities with edges between cities showing the costs of traveling between city pairs
Q1
Show how the uniform-cost search would progress with this map of cities. Start city is A and the goal city is G. 
- Add starting node to open list
- add connected nodes to open list, starting node to closed list
- "open" the node with the lowest value, and add it to the closed list
- Continue until a path is found
Two strategies to shorten computation
- Compare new node with closed list, don't add if already there
- Compare new node with open list, remove longer node
Q2
Repeat the search process as in question 1, but handle duplicate states 
- This seemed the most intuitive way to do it
Q3
Repeat the search process using BFS 
- Add each level of the tree into memory until all paths are stored.
- This is a huge number of computations, even for this simple case.
- Optimizations like dead ending at duplicate nodes would be required to make this manageable
Q4
Repeat the search process using DFS 
- Without cycle detection, this quickly falls into an infinite loop
- With cycle detection, its still a similar amount of computations as BFS, but without the insane memory requirement
Q5
Intuitively find the least cost from A to G. Which search algorithm is the shortest (expands the fewest nodes)?
- Shortest path: A->B->E->H
- I found my intuition was most similar to uniform cost with the two strategies outlined in question 1
- BFS, DFS have to search all possible paths to find shortest, uniform cost does not