@article {pub4996pub5257,
	title = {Right Place, Right Time: Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty},
	author = {Charlie Street AND Bruno Lacerda AND Manuel M{\"u}hlig AND Nick Hawes},
	year = {2024},
	month = {January},
	abstract = {For many multi-robot problems, tasks are announced during execution, where task an-nouncement times and locations areuncertain.  To synthesise multi-robot behaviour that isrobust to early announcements and unexpected delays, multi-robot task allocation methodsmust explicitly model the stochastic processes that govern task announcement.  In this pa-per, we model task announcement using continuous-time Markov chains which predict whenand where tasks will be announced.  We then present a task allocation framework whichuses the continuous-time Markov chains to allocate tasksproactively, such that robots arenear or at the task location upon its announcement.  Our method seeks to minimise theexpected total waiting durationfor each task, i.e.  the duration between task announcementand a robot beginning to service the task. Our framework can be applied to any multi-robottask allocation problem where robots complete spatiotemporal tasks which are announcedstochastically.  We demonstrate the efficacy of our approach in simulation, where we out-perform baselines which do not allocate tasks proactively, or do not fully exploit our taskannouncement models.},
	publisher = {AI Access Foundation},
	journal = {Journal of Artificial Intelligence Research},
	pages = {137-171},
	chapter = {79}
}