计算具有均匀动作成本的规划
On Computing Plans with Uniform Action Costs
AI总结：
本文针对现实规划中动作成本均匀性的需求，将三种均匀性度量引入自动规划，提出基于规划的编译方法以字典序优化成本总和与均匀性，实验验证了该方法能有效生成均匀计划。
摘要：
在许多现实世界的规划应用中，智能体可能对寻找动作成本尽可能均匀的计划感兴趣。这样的计划为智能体提供了一种稳定性和可预测性的感觉，当人类是执行规划工具建议的计划的智能体时，这些是关键特征。本文将三种均匀性度量应用于自动规划，并引入了基于规划的编译方法，允许按字典顺序优化动作成本之和与动作成本的均匀性。在知名和新颖的规划基准上的实验结果表明，重新表述的任务在实践中可以有效地解决，以生成均匀的计划。
英文摘要：
In many real-world planning applications, agents might be interested in finding plans whose actions have costs that are as uniform as possible. Such plans provide agents with a sense of stability and predictability, which are key features when humans are the agents executing plans suggested by planning tools. This paper adapts three uniformity metrics to automated planning, and introduce planning-based compilations that allow to lexicographically optimize sum of action costs and action costs uniformity. Experimental results both in well-known and novel planning benchmarks show that the reformulated tasks can be effectively solved in practice to generate uniform plans.
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。
