Robustness in Probabilistic Temporal Planning

Flexibility in agent scheduling increases the resilience of temporal plans in the face of new constraints. However, current metrics of flexibility ignore domain knowledge about how such constraints might arise in practice, e.g., due to the uncertain duration of a robot’s transition time from one location to another. Probabilistic temporal planning accounts for actions whose uncertain durations can be modeled with probability density functions. We introduce a new metric called robustness that measures the likelihood of success for probabilistic temporal plans. We show empirically that in multi-robot planning, robustness may be a better metric for assessing the quality of temporal plans than flexibility, thus reframing many popular scheduling optimization problems.