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规划感兴趣。此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，这些特性尤为关键。本文适配了三种均匀性度量指标至自动化规划，并引入了基于规划的编译方法，以词典序方式优化动作成本总和与动作成本均匀性。在已知及新型规划基准上的实验结果表明，重新表述的任务可在实践中有效求解，从而生成均匀规划。