notes.txt

How to solve a problem:
{
 0: Don't Panic,
 1: What are the inputs?,
 2: What are the outputs?,
 3: Work through some examples by hand (future test cases),
 4: Simple mechanical solution,
 5: Develop incrementally and test as you go
}

Don't optimize prematurely!


Definition of a Problem
- Initial State
- Actions(s)
- Result
- Goal Test(s)

- Path Cost
- Step Cost

Example with Problem: flight from Arad to Bucharest
Initial State: in Arad
Goal Test: True if in Bucharest
Actions: Follow path to connected city
  Arad: 0, Zerind: 1, Sibiu: 1, Rimnicu: 2, etc.


Uniform Cost Search (cheapest path first)
- pick the path with lowest cost
- keep going and summing subtotal costs
- explored, frontier, unexplored
https://www.youtube.com/watch?time_continue=1&v=MoyBcrw-n_M

Uniform Cost search
- expands out equally in all directions, may expend additional effort getting to a fairly direct path to the goal.

Greedy best-first search
- expands outward toward locations estimated as closer to the goal. If a direct path is available, expends much less effort than Uniform Cost; however, it does not consider any routes in which it may need to temporarily take a further away path in order to arrive at an overall shorter path.

A* Search
- utilizes both of these - will try to optimize with both the shortest path and the goal in mind.


A* Search

f = g + h
g(path) = path cost
h(path) = h(state) = estimated distance to goal
minimize g: keeps path short
minimize h: keeps focus on goal