Practical approach to self-constructive learning on subjects on computer science

我们在这个项目中获得的结果中存在一个框架，可以产生某些组合难题的实例。为了探索这样的框架，我们关注某些类型的谜题，这些谜题要求将数字分配给 nxn 网格的单元格，使其满足某些约束以及拉丁方条件，即每行和每列包含 {1, 2,…,n} 中的所有数字。我们基于该框架的算法自动生成谜题实例，可以通过算法内置的谜题推理规则来调整其解决的难度。我们进行了实验来证明，正如预期的那样，人类解算者倾向于正确解决使用简单推理规则生成的谜题实例，而他们往往无法解决使用复杂规则生成的谜题实例。

Among results we obtained in this project there exists a framework that yields instances of certain combinatorial puzzles. To explore such a framework, we focus on certain types of puzzles that ask an assignment of numbers to cells of nxn grids so that it satisfies certain constraints as well as the Latin square condition, that is, each row and column contains all of the numbers in {1, 2,…,n} Our algorithm based on the framework yields automatically puzzle instances whose difficulties to solve can be adjusted by means of puzzle inference rules built into the algorithm. We performed experiments to demonstrate that, as is expected, human solvers tend to solve puzzle instances correctly that are produced with easy inference rules, whereas they tend to fail to solve those produced with sophisticated rules.