T&Aelig;MS: a framework for environment centered analysis and design of coordination mechanisms
Foundations of distributed artificial intelligence
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Incorporating Uncertainty in Agent Commitments
ATAL '99 6th International Workshop on Intelligent Agents VI, Agent Theories, Architectures, and Languages (ATAL),
Modeling Uncertainty and its Implications to Sophisticated Control in Tæms Agents
Autonomous Agents and Multi-Agent Systems
Distributed management of flexible times schedules
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
From precedence constraint posting to partial order schedules: A CSP approach to Robust Scheduling
AI Communications - Constraint Programming for Planning and Scheduling
Solving generalized semi-Markov decision processes using continuous phase-type distributions
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Exploration of the robustness of plans
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Probabilistic temporal planning with uncertain durations
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Engineering a conformant probabilistic planner
Journal of Artificial Intelligence Research
Proactive algorithms for job shop scheduling with probabilistic durations
Journal of Artificial Intelligence Research
Dynamic control of plans with temporal uncertainty
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Drake: an efficient executive for temporal plans with choice
Journal of Artificial Intelligence Research
Determining the value of information for collaborative multi-agent planning
Autonomous Agents and Multi-Agent Systems
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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 in both the duration and outcomes of executed activities. To ensure scalability, our solution approach takes as its starting point an initial deterministic schedule for the agents, computed using expected duration reasoning. This initial agent schedule is probabilistically analyzed to find likely points of failure, and then selectively strengthened based on this analysis. For each scheduled activity, the probability of failing and the impact that failure would have on the schedule's overall reward are calculated and used to focus schedule strengthening actions. Such actions generally entail fundamental trade-offs; for example, modifications that increase the certainty that a high-reward activity succeeds may decrease the schedule slack available to accommodate uncertainty during execution. We describe a principled approach to handling these trade-offs based on the schedule's "expected reward," using it as a metric to ensure that all schedule modifications are ultimately beneficial. Finally, we present experimental results obtained using a multi-agent simulation environment, which confirm that executing schedules strengthened in this way result in significantly higher rewards than are achieved by executing the corresponding initial schedules.