Towards a Model of Puzznic

TL;DR

This paper compares planning and constraint programming methods for solving Puzznic levels without moving blocks, showing planning currently performs better but outlining future improvements for constraint models.

Contribution

It introduces a comparative analysis of planning and constraint programming approaches for Puzznic, highlighting current strengths and proposing enhancements for constraint models.

Findings

Planning approach outperforms constraint programming on benchmark levels.

Constraint models have potential for improvement with proposed modifications.

Study focuses on static levels with no moving blocks.

Abstract

We report on progress in modelling and solving Puzznic, a video game requiring the player to plan sequences of moves to clear a grid by matching blocks. We focus here on levels with no moving blocks. We compare a planning approach and three constraint programming approaches on a small set of benchmark instances. The planning approach is at present superior to the constraint programming approaches, but we outline proposals for improving the constraint models.