Using Adaptive Priority Weighting to Direct Search in Probabilistic Scheduling

Many scheduling problems reside in uncertain and dynamic environments – tasks have a nonzero probability of failure and may need to be rescheduled. In these cases, an optimized solution for a short-term time horizon may have a detrimental impact over a broader time scale. We examine a scheduling domain in which time and energy on a phased array radar system is allocated to track objects in orbit around the earth. This domain requires probabilistic modeling to optimize the expected number of successful tasks on a particular day. Failed tasks must be attempted again on subsequent days. Given a set of task requests, we study two long-term objectives: percentage of requests initially successful, and the average time between successful request updates. We investigate adaptive priority weighting strategies that directly influence the short-term objective function and thus indirectly influence the long-term goals. We find that adapting priority weights based on when individual tasks suc...

• Type: Conference
• Year: 2007
• Where: AIPS
• Authors: Andrew M. Sutton, Adele E. Howe, L. Darrell Whitley
• Keywords: AIPS 2007, Artificial Intelligence, Broader Time Scale, Long-term Objectives, Short-term Time Horizon