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

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

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

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

🧩Using backtracking algorithm to solve binary puzzles

Java Sudoku solver using AC3, Forward checking and Backtracking algorithms

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

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

AI kakuro solver

Using CSP algorithm with Forward Checking for solving Sudoku Puzzle

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

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

Implementation of Constraint Solvers in Java

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

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

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

A Kenken puzzle generator & solver

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 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.