Development of Iterative Real-Time Scheduler to Planner Feedback

Abstract

Planning for real-time applications involves decisions not only about what actions to take in what states to progress toward achieving goals (the traditional decision problem faced by AI planning systems), but also about how to realize those actions within hard real-time deadlines given the inherent limitations of an execution platform. Determining how to arrange actions in a sequence such that timely execution is guaranteed within constraints is a manifestation of the scheduling problem. All cases of the scheduling problem in any domain of nontrivial complexity are difficult to solve (NP-Hard). To more efficiently solve the real-time plan scheduling problem, we propose and analyze an iterative feedback/constraint relaxation method in which a scheduler and planner iteratively interact to efficiently develop a well-utilized schedule which includes as many planned actions as possible. This method has been successfully implemented within the Cooperative Intelligent Real-time Control Archi...