Generalized Constraint Satisfaction

This code attempts to solve a highly generalized constraint satisfaction problem by use of recursive backtracking and heavy domain pruning.

The pruning is in two parts - forward looking, and sorting the domains in order of least to most constricting.

Almost all of the program customization is contained in these lines::

variables = ['A', 'B', 'C', ...]
Vrange = (1,120)
assignment = {}
constraints = [...]
numConstraints = 5  # Number of constraints to actually use
numVariables = 6   # Number of variables to actually use

Variables are characters, assign them into the variables array. Vrange is two numbers, you input the min and max for any value a variable can be. Assignment{} is where the final results are stored, you can input values here if you wish to predetermine a variable value. Example usage: assignment = {'A': 5, ‘B’: 5}

To use this program, constraints need to be coded into the program. They are all in the format of :: (lambda x: x['A'] == x['B']**2 - x['C']**2, ['A', 'B', 'C']). This is a tuple where the first part is the function/constraint itself and the second part names each variable that this constraint acts on.

You can give your variables a min/max by modifying Vrange(_, _), where the first number is the min and the second is the max. Currently the range for all variables is 1-120.

You can then decide how many of your constraints you want to use, and how many variables you select. If a constraint is selected that contains a nonexistent variable, it will not be applied, so you do not need to worry about input validation on that level.

There are two .py files here, “LauncherVersion.py” only exists to fulfill specific requirements I was given that this program needed to satisfy. It breaks the problem up into 3 parts, and solves for 4-5 variables at a time (all solutions), and uses those domains to create the next "level”of solutions. This is a good solution, but only works for constraints that are applied hierarchically.