TY - GEN
T1 - Using adaptive priority weighting to direct search in probabilistic scheduling
AU - Sutton, Andrew M.
AU - Howe, Adele E.
AU - Whitley, L. Darrell
PY - 2007
Y1 - 2007
N2 - 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 succeed or fail allows a catalog of requests to be filled more quickly. Furthermore, with adaptive priorities, we observe a Pareto-front effect between the two long-term objectives as we modify how priorities are weighted, but an inverse effect of weighting when the priorities are not adapted.
AB - 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 succeed or fail allows a catalog of requests to be filled more quickly. Furthermore, with adaptive priorities, we observe a Pareto-front effect between the two long-term objectives as we modify how priorities are weighted, but an inverse effect of weighting when the priorities are not adapted.
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M3 - Conference contribution
AN - SCOPUS:52249116373
SN - 9781577353447
T3 - ICAPS 2007, 17th International Conference on Automated Planning and Scheduling
SP - 320
EP - 327
BT - ICAPS 2007, 17th International Conference on Automated Planning and Scheduling
PB - Association for the Advancement of Artificial Intelligence, AAAI
T2 - ICAPS 2007, 17th International Conference on Automated Planning and Scheduling
Y2 - 22 September 2007 through 26 September 2007
ER -