ntrang086 / constraint_satisfaction_Nqueens

an artificial intelligence project to solve the N-queen constraint satisfaction problem

Constraint Satisfaction in the N-Queens problem

Introduction

Constraint Satisfaction is a technique for solving problems by expressing limits on the values of each variable in the solution with mathematical constraints. For example, constraints in the Sudoku project are enforced implicitly by filtering the legal values for each box, and the planning project represents constraints as arcs connecting nodes in the planning graph. In this project we will use SymPy, a symbolic math library, to explicitly construct binary constraints and then use Backtracking to solve the N-queens problem (which is a generalization 8-queens problem). Using symbolic constraints makes it easier to visualize and reason about the constraints (especially for debugging), but comes with a performance penalty. See the Sympy_Intro notebook in the same directory for example code on sympy.

Briefly, the 8-queens problem asks us to place 8 queens on a standard 8x8 chessboard such that none of the queens are in "check" (i.e., no two queens occupy the same row, column, or diagonal). The N-queens problem generalizes the puzzle to to any size square board.

This project consists of three main steps:

• Step 1: Implement the NQueensCSP class to develop an efficient encoding of the N-queens problem and explicitly generate the constraints bounding the solution
• Step 2: Implement the search functions for recursive backtracking
• Step 3: Solve the N-queens problem

Code

• AIND-Constraint_Satisfaction.ipynb - Code to solve the N-queens problem
• util.py - Helper code to create constraints and visualize solutions
• Sympy_Intro.ipynb - Example code of sympy to show how it works

Setup

• Python 3
• numpy
• sympy
• matplotlib

Run

To run any script file, use:

python <script.py>

To open a notebook, use:

jupyter notebook <notebook.ipynb>