Evaluating Action-Based Temporal Planners Performance in the RoboCup Logistics League

Due to increased demands related to flexible product configurations, frequent order changes, and tight delivery windows, there is a need for flexible production using AI methods. A way of addressing this is the use of temporal planning as it provides the ability to generate plans for complex goals while considering temporal aspects such as deadlines, concurrency, and durations. A drawback in applying such methods in dynamic environments is their high and unpredictable planning time. In this paper, we present an evaluation of the current state-of-the-art temporal planners within the RoboCup Logistics League. Among the many factors that impact automated planners applicability, the level of abstraction of the planning model is paramount. We center our study on the effect that modeling choices have on the performance of the assessed planners. Our experimental results suggest that seeking for the right level of abstraction of planning domain models allows for compromising solutions between plan quality and plan solving time.