The Impact of Balancing on Problem Hardness in a Highly Structured Domain

Random problem distributions have played a key role in the study and design of algorithms for constraint satisfaction and Boolean satisfiability, as well as in our understanding of problem hardness, beyond standard worst-case complexity. We consider random problem distributions from a highly structured problem domain that generalizes the Quasigroup Completion problem (QCP) and Quasigroup with Holes (QWH), a widely used domain that captures the structure underlying a range of real-world applications. Our problem domain is also a generalization of the well-known Sudoku puzzle: we consider Sudoku instances of arbitrary order, with the additional generalization that the block regions can have rectangular shape, in addition to the standard square shape. We evaluate the computational hardness of Generalized Sudoku instances, for different parameter settings. Our experimental hardness results show that we can generate instances that are considerably harder than QCP/QWH instances of the same ...

• Type: Conference
• Year: 2006
• Where: AAAI
• Authors: Carlos Ansótegui, Ramón Béjar, Cèsar Fernández, Carla P. Gomes, Carles Mateu
• Keywords: AAAI 2006, Intelligent Agents, Problem Hardness, Random Problem Distributions, Sudoku Instances