This repository contains a tiny framework for solving constraint satisfaction problems (CSP) that have both discrete and finite domains. Although this framework is fully functional, it is not meant to be production-ready, as it is primarily a showcase for myself on how to use the functional capabilities of Java 8 to a large extent.
The implementation is largely inspired from the splendid introductory text "Artificial Intelligence - A Modern Approach" by Stuart Russell and Peter Norvig.
csp-core: Contains a functional approach on CSP-framework design for CSPs that are both discrete and finite.csp-sudoku: Contains an example application which expresses Sudoku as a CSP and solves it usingcsp-core.
The framework builds upon a set of a few abstractions. Class CSP is the abstract base class for all domain-specific
CSPs. It provides the means to construct the initial assignment, which is comprised of all the Variables of the CSP
and their initial state. A variable can be either assigned or unassigned. In the first case, its domain has been
reduced to a single fixed value, while in the latter case it has no assigned value, but a - possibly reduced - set
of admissible domain values. Class CSP also provides the means to construct the full set of Constraints for
the CSP. A Variable of the CSP takes part in one or multiple Constraints. A Constraint involves some subset
of the variables of a CSP and specifies the allowable combinations of values for that subset. The Constraint
class provides the means to determine whether a constraint is consistent and satisfied given the set of
dependent variables.
The current state of a CSP is represented using an Assignment. An assignment always contains the full set of
variables of the CSP. An Assignment is partial, if it still contains Variables that are unassigned. An assignment
that does not violate any constraints is called consistent or legal. A complete assignment is one in which
every variable is assigned. A solution to the CSP is a complete assignment which does not violate any constraints.
Class Assignment implements forward checking. This is a technique that eliminates the value assigned to a variable
from all other variables that participate in the same Constraints, thus further decreasing the search space of CSP.
Class DFSSolver provides a generic way to operate on instances of CSP using depth-first search. It is able to apply
heuristics for both variable ordering and value ordering that can dramatically decrease the search space. By default, it
uses an uninformed approach that simply selects the next unassigned variable and the preserves the original ordering of
domain values for that variable. The DFSSolver progresses from Assignment to its successor until a complete
assignment has been found or the search space is exhausted.
The framework currently only provides the AllDiff constraint. This constraint is satisfied if each of its variables is
assigned to a different value.
The DFSSolver uses an uninformed approach by default which simply selects the next unassigned variable. However,
it is also possible to use the MinimumRemainingValue heuristic, which selects the variable that is most constrained
given the current state of the CSP. Thus, the variable that has the fewest choices for domain values left will be
chosen. Using this heuristic can dramatically reduce the runtime of the solver, since the search space is pruned
efficiently.
The DFSSolver uses an uninformed approach by default which simply preserves the original ordering of domain values
for a given unassigned Variable. Currently, there is no informed implementation of ValueOrdering.
Module csp-sudoku formulates Sudoku as a constraint satisfaction problem. The current implementation is able to parse
a Sudoku puzzle as line-delimited string like the one shown underneath.
003020600
900305001
001806400
008102900
700000008
006708200
002609500
800203009
005010300
Each cell of the Sudoku puzzle is represented as a variable of the CSP, where
0 denotes an unassigned variable with domain values ranging from 1 to 9 and where every other number represents an
assigned variable. There are three kinds of constraints, which are all represented using AllDiff on their dependent
variables:
- Row constraints: The assigned values to every variable in a row of the puzzle must be all different.
- Column constraints: The assigned values to every variable in a column of the puzzle must be all different.
- Grid constraints: The assigned values to every variable within a grid must be all different.
In total there are 27 constraints and 81 variables.
This software is released under the terms of the MIT license.