ANSWER SET PROGRAMMING IN SOLVING IRREGULAR SUDOKU PUZZLES

These Irregular Sudoku puzzles are an interesting variant for its analysis using a prominent knowledge representation language with roots in logic programming. This language is well-known as Clingo isbased on answer set programming, it is based on the stable model semantics of logic programming. We propose an ASP-based method for generating and solving irregular NxN Sudoku using Clingo, focusing on how non-standard regions impact constraint design and solver efficiency. The model supports arbitrary polyomino-style zones, validating region structure and enforcing Sudoku rules without relying on fixed sub grids. By optimizing grounding, reducing redundant constraints, and applying region-aware heuristics, the system mitigates the combinatorial cost introduced by irregular layouts. In addition, an integrated system is built using Python and Godot as Front-End and Clingo as Back-End. Experimental evaluation across 2400 instances of 4x4 and 9x9 puzzles demonstrates that our approach maintains high solving efficiency, with irregular puzzles showing only a 4.9%.