Flexible Execution of Plans with Choice and Uncertainty

Abstract

Dynamic plan execution strategies allow an autonomous agent to respond to uncertainties, while improving robustness and reducing the need for an overly conservative plan. Executives have improved robustness by expanding the types of choices made dynamically, such as selecting alternate methods. However, in some approaches to date, these additional choices often induce significant storage requirements to make flexible execution possible. This paper presents a novel system called Drake, which is able to dramatically reduce the storage requirements in exchange for increased execution time for some computations. Drake frames a plan as a collection of related Simple Temporal Problems, and executes the plan with a fast dynamic scheduling algorithm. This scheduling algorithm leverages prior work in Assumption-based Truth Maintenance Systems to compactly record and reason over the family of Simple Temporal Problems. We also allow Drake to reason over temporal uncertainty and choices by using prior work in Simple Temporal Problems with Uncertainty, which can guarantee correct execution, regardless of the uncertain outcomes. On randomly generated structured plans with choice, framed as either Temporal Plan Networks or Disjunctive Temporal Problems, we show a reduction in the size of the solution set of around four orders of magnitude, compared to prior art.