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AUT Principles and Applications of Artificial Intelligence course (Fall 2020) projects
Solve Sudoku with Python using the CSF approach
second project at AUT Artificial Intelligence course (Fall 2020)
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.
A futoshiki solver tool using backtracking with some pruning methods.
CSP-Sudoku solver
Solve Sudoku as a Constraint Satisfaction Problem
we slove sudoku tables as a CSP problem using Backtrack algorithm with forward Checking and MRV and Degree heuristics.
This project solves a Futoshiki Board given an input files using the least number of moves. Backtracking, forward checking, the mrv heuristic, and the degree heuristic was used.
Backtracking Algo optimized with forward checking, MRV heuristics, degree heuristics and LCV-heuristics
This is a sudoku solver by doing backtracking search with forward checking using an MRV and degree heuristic. This was worked on in a group of myself and two other members.
An AI for solving Sudoku. 🤖 ⚡️
A program to solve Sudoku with Backtracking.
Resolver de palavras cruzadas com CSP, usando backtracking e heurísticas para preenchimento eficiente de grids.
Solving Sudoku puzzle with CSP