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.

翻译：在许多实际规划应用中，智能体可能希望找到动作成本尽可能均匀的规划。这类规划为智能体提供了稳定性和可预测性，当人类是执行规划工具所建议规划的智能体时，这些是关键特征。本文将三种均匀性度量标准适配到自动规划中，并引入基于规划的编译方法，从而能够按字典序优化动作成本之和与动作成本均匀性。在知名和新颖的规划基准上的实验结果表明，经过重新表述的任务在实践中能够有效求解，以生成均匀规划。