All programs contained in this repo must be run using python 2.7.9 and not compatible with python 3.x
In /alpha-beta run the nim program using
python nim.py --piles <pile 1 size> <pile 2 size> ... <pile N size> --first-move <human || computer>
Follow the prompts on how to move, i.e. <pile to choose from> <number of items to take as integers delimited by a single space. The game board will be printed between each move to display what options are available.
The program uses alpha-beta pruning to generate a game tree for nim, where the computer will then follow any existing winning strategy in the sub-tree of the current game state.
In /search run the npuzzle program using python npuzzle.py. The settings, which are predetermined, will choose 5 randomly generated 8-puzzle states and run A* and Greedy Best First Search on each state using one of three heuristic functions; these are number of tiles out of place, manhattan distance, and euclidean distance. Trial results including the explored and frontier set size, and the solution length will be printed to stdout. Each randomly generated state is ensured to belong to the same subset of n-puzzle states as the goal state.
In /genetic-algorithms run the knapsack approximation program using python knapsack.py. The tuning parameters that can be altered are found in main and inlclude: population size; number of generations to run; maximum weight allowed in the knapsack; fitness function; etc. To use your own knapsack, or item/weight combinations, define it in pregenerated_bags.py following the convention used within this file then change the import line in the main program to from pregenerated_bags import <yourknapsack> as knapsack.
write up to be added later