Artificial Intelligence - Special issue on knowledge representation
T&Aelig;MS: a framework for environment centered analysis and design of coordination mechanisms
Foundations of distributed artificial intelligence
Gaining efficiency and flexibility in the simple temporal problem
TIME '96 Proceedings of the 3rd Workshop on Temporal Representation and Reasoning (TIME'96)
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Distributed management of flexible times schedules
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part I
Determining the value of information for collaborative multi-agent planning
Autonomous Agents and Multi-Agent Systems
Distributed reasoning for multiagent simple temporal problems
Journal of Artificial Intelligence Research
Group planning with time constraints
Annals of Mathematics and Artificial Intelligence
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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 the distributed problem solving setting we consider, each agent begins with a task itinerary, and, as execution unfolds and dynamics ensue (e.g., tasks fail, new tasks are discovered, etc.), agents must coordinate to extend and revise their plans accordingly. The team objective is to maximize the utility accrued from executed actions over a given time horizon. Our approach to solving this problem is based on distributed management of agent schedules. We describe an agent architecture that uses the synergy between intra-agent scheduling and inter-agent coordination to promote task allocation decisions that minimize travel time and maximize time available for utility-acrruing activities. Experimental results are presented that compare our agent's performance to that of an agent using an intelligent dispatching strategy previously shown to outperform our approach on synthetic, stateless, utility maximization problems. Across a range of problems involving a mix of situated and non-situated tasks our advance scheduling approach dominates this same dispatch strategy. Finally, we report performance results with an extension of the system on a limited set of field test experiments.