Applying Swarm Intelligence to Solve Heyawake Puzzles


Many real world problems face multiple constraints in the environment, in which some of the partitions have more defined constraints than others. Those with less restriction possess flexibility, but make identification of the global solution difficult due to a bigger search space. The Heyawake puzzle game is a good representative of such, proven to be a NP-complete problem. Each puzzle is composed of rooms that contain cells. The game is to fill up certain cells to satisfy a set of correlated rules. However, the major challenge comes from a freedom that the number of filled cells in a room may not be pre-defined but arbitrary. This freedom introduces complexity. In this paper, a heuristic learning solver, which applies the ant colony swarm intelligence technique, is developed to tackle the high computation complexity of Heyawake. A two-phase processing approach is employed to address firm values first and then account for the uncertainties next. Our experiments show that the method yields good performance on puzzles with more fixed restriction in rooms but can suffer from a computation overhead for many rooms with less restriction.

  • Abstract
  • 1. Introduction
  • 2. Methodology
  • 3. Experiments
  • 4. Conclusions
  • References

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