Terry L. Zimmerman

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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 consider the problem of coordinating a team of agents engaged in executing a set of inter-dependent, 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)
In this paper, we describe an incremental scheduling framework designed to support joint management of inter-dependent 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 probabilistic reasoning. Our specific focus is a class of oversubscribed scheduling problems where the goal is to maximize the reward earned by a team of agents in a distributed execution environment. There is uncertainty(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)
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 infra-structure repair(More)
In this paper, we describe an approach to scheduling under uncertainty that achieves scalability through a coupling of deterministic and probabilistic reasoning. Our specific focus is a class of oversubscribed scheduling problems where the goal is to maximize the reward earned by a team of agents in a distributed execution environment. There is uncertainty(More)
In this paper, we consider the role of meta-reasoning in achieving effective coordination among multiple agents in maintaining and executing joint plans in an uncertain environment. We assume that each agent has responsibility for performing a particular set of activities in the plan over time. Each agent is provided with an initial schedule and a set of(More)