Our Implementation of a MiniMax algorithm with alpha beta pruning in the context of an in-class competition. Our Algorithm finished in first place.
Install a Go toolchain (version >= 1.11 to support go modules).
All instructions can be found in the following link. All credits should be given to the github linked above.
To be noted that there are some issues/bugs but overall works well for testing. The official server is not made public and is, moreover, not available on Mac.
Usage of twilight:
total number of columns (default 10)
quantity of humans group (default 16)
path to the map to load (or save if randomly generating)
quantity of monster in the start case (default 8)
use a randomly generated map
total number of rows (default 10)
Running the Code
creating the map
Create a random map by running the following code in the twilight folder:
go run . -rand
execute the code
Run the code by executing the following line of code:
python main.py NAME HOST PORT
- PORT should be 5555
- HOST should be localhost
- NAME should be the name of your AI
Please note that 2 AIs should be launched for the server to run so either run you own AI and confront it with ours or run the AI twice.
A report describing our code can be found here
The rule book can be found here