ECS170 – Climbing Mt St Helens Instructions
Your assignment is to create an implementation of the AIModule interface that computes a path
from the start location to the end location while minimizing the total search space. Once you’ve
written this function, you can run your program through command line arguments, those being:
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– w (int) – width of the map (default 500)
– 1 (int) – length of the map (default 500)
– seed (int) – seed to set for random terrain generation (default None, set by random module)
– filename (string) – filename for npy file to load for map
– cost (string) – cost function. Values [‘exp’, ‘div’] accepted. Default ‘exp’
– AI (string) – Name of AIModule to use. Values [‘AStarExp’, ‘AStarDiv’, ‘AStartMSH’
‘Dijkstra’] accepted. Default ‘exp’.
The program can be executed using the above command-line instructions. For example, running
the following command:
>> python Main.py -seed 0 -cost exp -AI Djikstra
Will create a random terrain map of size 500×500 using the division cost function (see below)
and use the Dijkstra Al. While the terrain map is random, you can get the exact same terrain map
whenever you use the seed O. Note that length and width are not set and their default values are
To help test your implementation, we’ve provided a working Dijkstra’s algorithm Al class called
DijkstraAl. As you’ve learned in class, Dijkstra’s algorithm always yields the optimal path, so
you can compare your own Al against the DijkstraAl module to see if your path is indeed
optimal. Note that when we run your program for grading purposes, we will be using a fresh
copy of the provided starter code. Please do not modify the starter code. Otherwise, you may be
under the impression that your code is working properly but it will not work properly on our
Submission Instructions
Please submit the following:
AIModule_
Assignment1
As individual files. Please do not include any starter code or any other files, and replace
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