Sokoban的Initial States的程序生成
Procedural Generation of Initial States of Sokoban
摘要：
状态空间搜索问题的初始状态的过程生成在人类和机器学习以及规划系统的评估中有应用。在本文中，我们处理的任务，产生硬和可解的初始状态的推箱子难题。我们提出了基于模式数据库的硬度度量，并使用新颖性来改善搜索方法在生成初始状态的任务中的探索。然后，我们提出了一个名为Beta的系统，它使用我们的硬度指标和新颖性来生成初始状态。实验表明，Beta能够生成比人类专家设计的更难由专业求解器求解的初始状态。
英文摘要：
Procedural generation of initial states of state-space search problems have applications in human and machine learning as well as in the evaluation of planning systems. In this paper we deal with the task of generating hard and solvable initial states of Sokoban puzzles. We propose hardness metrics based on pattern database heuristics and the use of novelty to improve the exploration of search methods in the task of generating initial states. We then present a system called Beta that uses our hardness metrics and novelty to generate initial states. Experiments show that Beta is able to generate initial states that are harder to solve by a specialized solver than those designed by human experts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
