  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.
