PROJECT PYTHON CODING

For this project, you just use the same framework as project 1 but finish implementing the third function "astar()" in the "[login to view URL]". After finishing the code, you are required to provide document report (.pdf file) to demonstrate the advantage of A-star search against breadth first search. As discussed in the class, you can measure the time performance of searching the path (measured in milliseconds) between these two methods. To make a fair comparison, you have both search methods with the same starting state. To generate the same starting state, you can either modify the framework and set a fixed input puzzle every time. Or you can start with goal state when you play the puzzle. Then move the pieces (or tiles) randomly in several steps (e.g. 20), you then manually record these 20 steps. Then run bfs() first with a time recording Then repeat the same process by following the 20 steps to generate the same starting state and run astar(). Below are some example of comparison. The format is not necessary to strictly follow. You can provide your report in whatever format to demonstrate the comparison. But you are suggested to provide at least 3 cases (the more the better) to show the performance contrast and also try to make these cases have high diversity in terms of step # of the path, e.g. 10 steps, 50 steps, 120 steps. Case I: Starting state: 056421378 -> 012345678 BFS - 5000ms Astart() - 1200ms Case II: Starting state: 058124376 -> 012345678 BFS - 3000ms Astart() - 600ms