On Computing Plans with Uniform Action Costs
This addresses the need for stable and predictable plans in automated planning tools, especially when humans are involved, but it is incremental as it adapts existing metrics and methods.
The paper tackled the problem of generating plans with uniform action costs for stability and predictability in real-world applications, and introduced planning-based compilations that effectively solve reformulated tasks to produce such plans in benchmarks.
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.