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Perception-based constraint solving for sudoku images.

Maxime Mulamba1,2, Jayanta Mandi2, Ali İrfan Mahmutoğulları2

  • 1Data Analytics Laboratory, Vrije Universiteit Brussel, Pleinlaan 5, Brussels, 1050 Belgium.

Constraints : an International Journal
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Summary
This summary is machine-generated.

This study introduces a hybrid approach for perception-based constraint solving, integrating machine learning and constraint solving to interpret images for tasks like Sudoku. The method corrects errors and improves robustness, even with user mistakes.

Keywords:
Constraint programmingJoint inferenceMachine learningVisual sudoku

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Constraint Satisfaction

Background:

  • Perception-based constraint solving integrates user-provided images into problem specifications.
  • Neural networks interpret images to extract data, such as Sudoku grid values.
  • Existing methods struggle with noisy or erroneous image data.

Purpose of the Study:

  • Investigate a hybrid machine learning and constraint solving approach for joint inference.
  • Evaluate the impact of classifier calibration on joint inference performance.
  • Develop a robust framework to identify and correct user errors in perception-based constraint solving.

Main Methods:

  • A hybrid model combining machine learning for perception and constraint solving for reasoning.
  • Joint inference predicts blank cells and solves the puzzle simultaneously.
  • Classifier calibration techniques are applied to improve prediction accuracy.
  • Constraint satisfaction identifies inconsistencies arising from user errors.

Main Results:

  • Joint inference successfully corrects errors made by the perception classifier.
  • Classifier calibration enhances solution quality across various datasets.
  • The framework demonstrates robustness in handling user-written errors and distinguishing input types.

Conclusions:

  • Hybrid perception-based constraint solving offers a powerful approach for complex problems.
  • Classifier calibration is crucial for improving the accuracy of joint inference.
  • The developed system effectively handles real-world imperfections, including user errors, for robust problem-solving.