Dealing with Continuous Resources in AI Planning

This paper presents an approach towards probabilistic planning with continuous resources. It adopts stochastic concepts for continuous probabilities and integrates them into a STRIPS-based planning framework. The approach enables the construction of plans that are guaranteed to meet certain probability thresholds w.r.t. the consumption of critical resources. Furthermore, the consumption probabilities of multiple resources can be accumulated and thus an overall probability for a successful execution of an aggregate plan can be computed. We extend our approach to HTN-based planning and show how heuristics can be derived that lead to plans with a minimized average value/variance of their overall resource consumption.