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princeton.edu
Solving Sudoku Puzzles with Recurrent Neural Networks, Richard Zhu, UG '23 (3967706)
Recent advancements in machine learning have allowed for near-human or even superhuman performance in applications spanning chess-playing, protein folding, and natural language generation. Sudoku, a comparatively simpler task, appears to be an interesting problem. The puzzle can be modelled as a constraint or propositional satisfaction problem ...
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[In Addendum: CSPs] 🧠💡 Constraint Satisfaction Problems (CSPs) are at the heart of some of the smartest problem-solving techniques out there. They involve variables, domains, and constraints—rules that limit which values can go where—with the goal of assigning values that satisfy all the constraints ✅. Instead of guessing, CSP solvers use logic to eliminate impossible options 🧐. From Sudoku puzzles 📝 to scheduling 📅, route planning 🗺️, and AI decision-making 🤖, CSPs are everywhere, helpin
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