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• W2306338306 endingPage "233" @default.
• W2306338306 startingPage "233" @default.
• W2306338306 abstract "Humans are adept at cooperating and coordinating with one another in dynamic, uncertain environments while adapting to unexpected events and delays. Human workers are able to predict the likely outcome of their current tasks, communicate with one another to schedule and reschedule cooperative tasks, and fluidly join and leave teams as the situation warrants. This thesis investigates how to bring the fluidity, responsiveness, and reliability of human teams to a centrally coordinated, multi-robot team operating in domains requiring tight inter-robot coordination and either the maximization of overall reward or the minimization of makespan. Proactive replanning endeavors to create flexible teams, then exploit their adaptability by dynamically adjusting the teams' composition in response to predicted problems and opportunities. We have explored three components of proactive replanning: live duration prediction, mutable teams, and live task modification. We present algorithms to predict a distribution across the remaining duration of a task, given a measurement of the task's current state and a relatively sparse set of training data. By predicting the duration of tasks throughout their execution, we are able to execute schedules that achieve 45% of the improvement possible with an omniscient planner. Mutable teams are those that agents may join or leave throughout the cooperative task's execution. Mutable teams leverage the possibilities presented by optional roles: roles in a team that produce salutary effects, but are not strictly required for the task to proceed. By dramatically increasing the options available to the planner, the use of mutable teams allows the creation of significantly shorter schedules with 57% fewer planning iterations. Live task modification is the adjustment of tasks already underway by the planner, requiring close coordination between planner and executive. It is informed by duration prediction and uses mutable teams to adjust executing tasks in response to predicted problems and opportunities. We evaluate the effects of the three components of proactive replanning in isolation and in concert within a complex, stochastic, multi-agent simulated domain. We find that proactive replanning significantly reduces the makespan of executed schedules and increases the flexibility of the planner, bringing multi-robot systems one step closer to the fluid teamwork so effortlessly accomplished by humans." @default.
• W2306338306 created "2016-06-24" @default.
• W2306338306 creator A5064960456 @default.
• W2306338306 creator A5081590556 @default.
• W2306338306 date "2009-01-01" @default.
• W2306338306 modified "2023-09-22" @default.
• W2306338306 title "Proactive replanning for multi-robot teams" @default.
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• W2306338306 hasPublicationYear "2009" @default.
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