Nonogram Tournaments in TAAI 2011

Sun, Der-Johng; Wu, Kuang-che; Wu, I‐Chen; Yen, Shi-Jim; Kao, Kuo-Yuan

doi:10.3233/icg-2012-35210

Cited by 2 publications

(1 citation statement)

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“…In our experiments, two main sets of test puzzles were used for performance analysis. The first set contained 1000 25 25 puzzles generated at the TAAI 2011 Nonogram Solver Tournament [10], [16]. In this tournament, a nonogram random generator [23] was used to produce these puzzles at random, where the density of black cells in these 1000 puzzles ranged from 50% to 35%, in linearly decreasing order.…”

Section: Methodsmentioning

confidence: 99%

An Efficient Approach to Solving Nonograms

Sun

Chen

et al. 2013

IEEE Trans. Comput. Intell. AI Games

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A nonogram puzzle is played on a rectangular grid of pixels with clues given in the form of row and column constraints. The aim of solving a nonogram puzzle, an NP-complete problem, is to paint all the pixels of the grid in black and white while satisfying these constraints. This paper proposes an efficient approach to solving nonogram puzzles. We propose a fast dynamic programming (DP) method for line solving, whose time complexity in the worst case is only, where the grid size is and is the average number of integers in one constraint, always smaller than . In contrast, the time complexity for the best line-solving method in the past is . We also propose some fully probing (FP) methods to solve more pixels before running backtracking. Our FP methods can solve more pixels than the method proposed by Batenburg and Kosters (before backtracking), while having a time complexity that is smaller than theirs by a factor of . Most importantly, these FP methods provide useful guidance in choosing the next promising pixel to guess during backtracking. The proposed methods are incorporated into a fast nonogram solver, named LalaFrogKK. The program outperformed all the programs collected in webpbn.com, and also won both nonogram tournaments that were held at the 2011 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2011, Taiwan). We expect that the proposed FP methods can also be applied to solving other puzzles efficiently.

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Section: Methodsmentioning

confidence: 99%