Dealing with Continuous Resources in AI Planning
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Abstract 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.
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