# Solving Sudoku Using Probabilistic Graphical Models

@inproceedings{Khan2008SolvingSU, title={Solving Sudoku Using Probabilistic Graphical Models}, author={Sheehan Khan and Shahab Jabbari and Shahin Jabbari and Majid Ghanbarinejad}, year={2008} }

Sudoku is a popular number puzzle. Here, we model the puzzle as a probabilistic graphical model and drive a modification to the well-known sum-product and max-product message passing to solve the puzzle. In addition, we propose a Sudoku solver utilizing a combination of message passing and Sinkhorn balancing and show that as Sudoku puzzles become larger, the impact of loopy propagation does not increase.

## 11 Citations

Using Probabilistic Graphic Models to Solve NP-Complete Puzzle Problems

- 2015

Using Probabilistic Graphical Models to Solve NP-complete Puzzle Problems Probabilistic Graphical Models (PGMs) are commonly used in machine learning to solve problems stemming from medicine,…

Application of Message Passing and Sinkhorn Balancing Algorithms for Probabilistic Graphical Models

- Computer Science
- 2014

This project attempts to modify the sum-product message passing algorithm to solve Sudoku, and implements a solution using Sinkhorn balancing to overcome the impact of loopy propagation and compare its performance with the former.

An evolutionary approach to sudoku puzzles with filtered mutations

- Mathematics, Computer Science2015 IEEE Congress on Evolutionary Computation (CEC)
- 2015

This paper proposes a GA with sophisticated genetic mutations that mitigate the worsening of fitness values in extremely difficult sudoku puzzles, in which only a few given numbers are provided.

Finding solutions to sudoku puzzles using human intuitive heuristics

- Computer ScienceSouth Afr. Comput. J.
- 2012

The performance of the approach presented was found to be comparable to other methods used to solve Sudoku problems and in a number of cases produced better results.

Sudoku at the Intersection of Classical and Quantum Computing

- Mathematics
- 2014

Even when quantum computers are widely available, it is unlikely that we will simply use them to solve all our problems. Rather we would use each computer for the types of problems it is best suited…

Rešavanje problema CSP tehnikama svodjenja na problem SAT

- Computer Science
- 2016

The main aim of this thesis is to improve CSP solving techniques that are using reduction to SAT, and two new hybrid encodings of CSPs to SAT are presented and they combine good sides of the existingencodings.

A hardware generator for factor graph applications

- Computer Science2014 International Conference on ReConFigurable Computing and FPGAs (ReConFig14)
- 2014

A framework is presented for rapid prototyping of hardware implementations of FG-based applications, and the framework has been applied to construct a Sudoku solver.

Recurrent Relational Networks for Complex Relational Reasoning

- Computer ScienceArXiv
- 2017

R recurrent relational networks are introduced which increase the suite of solvable tasks to those that require an order of magnitude more steps of relational reasoning and are applied to the BaBi textual QA dataset solving 19/20 tasks.

Recurrent Relational Networks

- Computer ScienceNeurIPS
- 2018

The recurrent relational network is introduced, a general purpose module that operates on a graph representation of objects that can augment any neural network model with the capacity to do many-step relational reasoning.

End-to-end information extraction from business documents

- Computer Science
- 2019

A recurrent neural network model that can capture long range context and compare it to a baseline logistic regression model corresponding to the current CloudScan production system are described.

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