@inproceedings{chi_isairas2018_squeaky,
	title        = {Using Squeaky Wheel Optimization to Derive Problem Specific Control Information for a One Shot Scheduler for a Planetary Rover},
	author       = {Wayne Chi and Steve Chien and Jagriti Agrawal},
	year         = 2018,
	month        = {July},
	booktitle    = {International Symposium on Artificial Intelligence, Robotics, and Automation for Space (ISAIRAS 2018)},
	address      = {Madrid, Spain},
	url          = {https://ai.jpl.nasa.gov/public/papers/chi-icaps2018-squeaky.pdf},
	note         = {Also appears at 2018 International Conference on Planning and Scheduling Workshop on Planning and Robotics (ICAPS PlanRob 2018) and Workshop on Scheduling and Planning Applications (ICAPS SPARK 2018)},
	abstract     = {We describe the application of using Monte Carlo simulation to optimize a schedule for execution and rescheduling robustness and activity score in the face of execution uncertainties.  We apply these techniques to the problem of optimizing a schedule for a planetary rover with very limited onboard computation.  We search in the schedule activity priority space - where the onboard scheduler is (a) a one shot non-backtracking scheduler in which (b) the activity priority determines the order in which activities are considered for placement in the schedule and (c) once an activity is placed it is never moved or deleted.  We show that simulation driven search outperforms a number of alternative proposed heuristic static priority assignment schemes.  Our approach can be viewed using simulation feedback to determine problem specific heuristics much like squeaky wheel optimization.},
	clearance    = {CL\#18-2732},
	project      = {m2020\_simple\_planner}
}

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