Solves KenKen puzzles, by representing it as a Constraint Satisfaction Problem (CSP)

Artificial Intelligence Course 3rd Project: Implementing CSP Backtracking, Forward Checking and MAC Algorithms in order to solve a binary puzzle

Example lecture for Constraint Satisfaction Problems in an interactive jupyter notebook. With python code to solve CSPs, with visualization of Sudoku and NQueens problems.

This repository contains generic platform for solving and benchmarking computational puzzles using different search strategies

🧩Using backtracking algorithm to solve binary puzzles

🔢 A parallelized Sudoku solver implemented with various solving algorithms in C++

AI kakuro solver

Java Sudoku solver using AC3, Forward checking and Backtracking algorithms

AUT Principles and Applications of Artificial Intelligence course (Fall 2020) projects

A Kenken puzzle generator & solver

Using CSP algorithm with Forward Checking for solving Sudoku Puzzle

All AUT's principles and applications of artificial intelligence course projects.

Project to learn and understand backtracking algorithms with the goal to solve a crossword puzzle

Implementation of Constraint Solvers in Java

simple Java implementation of nonogram solver based on constraint satisfaction programming using several heuristics

A solution centered around AI-driven scheduling, designed to optimize staff allocation, enhance patient care, and streamline hospital operations. It integrates advanced scheduling algorithms that prioritize staff preferences, ensure designated days off, and dynamically allocate doctors, nurses, and receptionists based on demand and availability.

EasyCSP is an open-source Java library for Constraint Satisfaction Programming. Supports CSPs, CSOPs, discrete object domains, int interval domains, int constraint binarization. Examples include NQueens, Sudoku. (Builds on Sourceforge)

Python code to solve Jigsaw Sudoku using backtracking with look forward (Forward Checking) and model dependant ordinal strategies

Project focuses on optimised implementation of Backtracking and Forward Checking algorithms in order to find all solutions of the N Queens problem

CSP-Sudoku solver

A futoshiki solver tool using backtracking with some pruning methods.

This project is a sudoku-solver implement by Constraint satisfaction problem. We add the colour option to our sudoku problem as if the number of a place is bigger than other neighbours, the colour of that place must be higher in a given colour's priority. We use the Constraint satisfaction problem(CSP), as we said before, in additional apply degree and MRV heuristic for selecting a variable. After we assigned a value to the selected variable, apply forward-checking to update the domain of variables. If there is a solution for the sudoku problem, we report it; otherwise, the search is unsuccessful, and we have not a solution.

kakuro (simple version) solver as CSP problem - Artificial Intelligence course project - Spring 2021

sudoku resolving using AI algorithms

solving sudoku problems with csp

This repository is projects of Artificial Intelligence course

This code was submitted as programming project two for ITCS 6150 Intelligent Systems under Dr. Dewan Ahmad at the University of North Carolina at Charlotte for the fall 2021 semester. The aim is to solve the constraint satisfaction problem of map coloring for the USA and Australia.

Solving resource management problem using backtracking,back jumping and forward checking. Edit Add topics

Contains notebook implementations for the AI based assignments using graph based algorithms that are commonly used in solving AI based problems. Algorithms include BFS, DFS, Hill Climbing, Differential Evolution, Genetic, Back Tracking..

Implementing CSP with Forward Checking constraint propagation on the example of solving Binary and Futoshiki puzzle