This is an implementation of alpha-beta pruning on Python. Alpha-Beta pruning is an algorighm to reduce searching space on minimax searching tree. This program has a text file as an input parameter and generates result text file.
- Source code
- 5 basic test cases
- 50 big datasets
Refer csci561_summer2018_hw1.pdf
Input file
DAY
Contains “today” or “yesterday” indicating which day the RPL was posted
PLAYER
Contains either “R1” or “R2” indicating which roommate has next turn
REGION PROFITABILITY LIST
Ordered list of tuples (Region_Identifier, Profit_Number).
ADJACENCY MATRIX ROWS
The rows of the adjacency matrix representing the map.
REGIONS PICKED SO FAR
Comma separated list of regions picked so far. Will contain “*” if no activity yet.
MAX DEPTH
Number that determines the maximum depth of your search tree
Output file
NEXT REGION TO PICK
The next region the PLAYER should pick.
UTILITY VALUE LIST
In the case of a current RPL: A comma separated list of the utility of the PLAYER at the terminal nodes of the search tree (considering the maximum depth of MAX DEPTH) for the activity under the assumption that both players select moves maximizing their utilities.
In the case of a stale RPL:
A comma separated list of the utility of the at the next pick nodes under the assumption that both players select moves maximizing their utilities according to the heuristic function.
*FILE SHOULD HAVE NO BLANK LINE
Python 2.7
$ python hw1cs561s18_alphabeta.py -i input1.txtThis is the first assignment of CSCI-561 Foundation of Artificial Intelligence, 2018 summer
Version 1.0