Sudoku Assistant – an AI-Powered App to Help Solve Pen-and-Paper Sudokus

Guns, Tias; Gamba, Emilio; Mulamba, Maxime; Bleukx, Ignace; Berden, Senne; Pesa, Milan

doi:10.1609/aaai.v37i13.27072

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The early work on MAPSEE (Mackworth 1977) used constraint satisfaction to interpret sketch maps (albeit represented as plotter commands). Guns et al (2023) worked with pictures of Sudoku puzzles.…”

Section: Natural Language Processingmentioning

confidence: 99%

Conversational Modeling for Constraint Satisfaction

Freuder

2024

AAAI

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Many problems, from Sudoku to factory scheduling, can be regarded as constraint satisfaction problems. A key component of real world problem solving is a conversation between a constraint programming expert and a problem domain expert to specify the problem to be solved. This presentation argues that the time is ripe for progress in automating the constraint programmer side of this conversation and suggests promising avenues for this pursuit.

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Section: Natural Language Processingmentioning

confidence: 99%

Conversational Modeling for Constraint Satisfaction

Freuder

2024

AAAI

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“…1. The rapidly evolving realm of computer vision has increasing in various aspects of our daily lives, encompassing domain such as image recognition [19], language translation [25], and critical medical applications like X-ray image analysis for disease diagnosis [10,27], and game playing [8]. The challenging of Sudoku puzzle has attracted researchers to leverage deep learning techniques for its solution.…”

Section: Introductionmentioning

confidence: 99%

Rule-based explaining module: Enhancing the interpretability of recurrent relational network in Sudoku solving

Cheewaprakobkit,

K. Shih,

Lau

et al. 2023

MG&V

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Computer vision has gained significant attention in the field of information technology due to its widespread application that addresses real-world challenges, surpassing human intelligence in tasks such as image recognition, classification, natural language processing, and even game playing. Sudoku, a challenging puzzle that has captivated many people, exhibits a complexity that has attracted researchers to leverage deep learning techniques for its solution. However, the reliance on black-box neural networks has raised concerns about transparency and explainability. In response to this challenge, we present the Rule-based Explaining Module (REM), which is designed to provide explanations of the decision-making processes using Recurrent Relational Networks (RRN). Our proposed methodology is to bridge the gap between complex RRN models and human understanding by unveiling the specific rules applied by the model at each stage of the Sudoku puzzle solving process. Evaluating REM on the Minimum Sudoku dataset, we achieved an accuracy of over 98.00%.

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Sudoku Assistant – an AI-Powered App to Help Solve Pen-and-Paper Sudokus

Cited by 2 publications

References 11 publications

Conversational Modeling for Constraint Satisfaction

Conversational Modeling for Constraint Satisfaction

Rule-based explaining module: Enhancing the interpretability of recurrent relational network in Sudoku solving

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