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Solving Nurikabe with Ant Colony Optimization

By Martyn Amos, Matthew Crossley and Huw Lloyd

Abstract

We present the first nature-inspired algorithm for the NP-complete Nurikabe pencil puzzle. Our method, based on Ant Colony Optimization (ACO), offers competitive performance with a direct logic-based solver, with improved run-time performance on smaller instances, but poorer performance on large instances. Importantly, our algorithm is “problem agnostic", and requires no heuristic information. This suggests the possibility of a generic ACO-based framework for the efficient solution of a wide range of similar logic puzzles and games. We further suggest that Nurikabe may provide a challenging benchmark for nature-inspired optimization

Topics: G400
Publisher: 'Association for Computing Machinery (ACM)'
Year: 2019
DOI identifier: 10.1145/3319619.3338470
OAI identifier: oai:nrl.northumbria.ac.uk:39017

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