Terry L. Zimmerman

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We consider the problem of coordinating a team of agents engaged in executing a set of interdependent , geographically dispersed tasks in an oversubscribed and uncertain environment. In such domains, where there are sequence-dependent setup activities (e.g., travel), we argue that there is inherent leverage to having agents maintain advance schedules. In(More)
We consider the problem of managing schedules in an uncertain, distributed environment. We assume a team of collaborative agents, each responsible for executing a portion of a globally pre-established schedule, but none possessing a global view of either the problem or solution. The goal is to maximize the joint quality obtained from the activities executed(More)
We present techniques for incrementally managing schedules in domains where activities accrue quality as a function of the time and resources allocated to them and the goal is to maximize the overall quality of actions executed over time. The scheduling problem of interest is both over-subscribed and dynamic; there is generally more to do than is possible(More)
In this paper, we describe an incremental scheduling framework designed to support joint management of interdependent schedules by multiple executing agents. We assume an uncertain execution environment and a distributed representation of the overall problem and schedule such that no single agent has a complete view. Hence as unexpected execution events(More)
In this paper, we describe an approach to scheduling under uncertainty that achieves scalability through a coupling of deterministic and probabilis-tic reasoning. Our specific focus is a class of over-subscribed scheduling problems where the goal is to maximize the reward earned by a team of agents in a distributed execution environment. There is(More)
We consider a scheduling problem where the goal is to maximize the reward obtained by a team of agents in an execution environment with duration and activity outcome uncertainty. To address scalability issues, we take as our starting point a deterministic, partial-order schedule and attempt to hedge against activity failure by adding redundant backup(More)
In this paper, we describe the application of a multi-agent framework for collaborative scheduling to a disaster response coordination problem. The target problem is a field exercise mockup of a natural disaster, where a team of human agents must rely on their respective automated scheduling agents to coordinate and accomplish various infrastructure repair(More)
In this paper, we describe an approach to scheduling under uncertainty that achieves scalability through a coupling of deterministic and probabilis-tic reasoning. Our specific focus is a class of over-subscribed scheduling problems where the goal is to maximize the reward earned by a team of agents in a distributed execution environment. There is(More)
We outline a approach for closely integrating classical automated planning with scheduling in a manner designed to maximize the opportunity for leveraging the strengths of each. A fundamental capability needed to implement the system is an efficient resource-lifted planning graph, and we introduce the process for achieving it here. We report experimental(More)