Real-time control has become increasingly important as technologies are moved from the lab into real world situations. The complexity associated with these systems increases as control and autonomy are distributed, due to such issues as precedence constraints, shared resources, and the lack of a complete and consistent world view. In this paper we describe a soft real-time architecture designed to address these requirements, motivated by challenges encountered in a distributed sensor allocation environment. The system features the ability to generate schedules respecting temporal, structural and resource constraints, to merge new goals with existing ones, and to detect and handle unexpected results from activities. We will cover a suite of technologies being employed, including quantitative task representation, alternative plan selection, partial-order scheduling, schedule consolidation and conflict resolution in an uncertain environment. Technologies which facilitate on-line real-time control, including schedule caching and variable time granularities are also discussed.
OverviewIn the field of multi-agent systems, much of the research and most of the discussion focuses on the dynamics and interactions between agents and agent groups. Just as *Effort sponsored in part by the Defense Advanced Research Projects Agency (DARPA) and Air Force Research Laboratory Air Force Materiel Command, USAF, under agreements number F30602-99-2-0525 and DOD DABT63-99-1-0004. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. This material is also based upon work supported by the National Science Foundation under Grants No. IIS-9812755 and IIS-9988784. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Furthermore, the views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Defense Advanced Research Projects Agency (DARPA), Air Force Research Laboratory or the U.S. Government. Copyright (E) 2002, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. important, however, is the design and behavior of the individual agents themselves. The efficiency of an agent's internal mechanics contribute to the foundation of the system as a whole, and the degree of flexibility these mechanics offer affect the agent's achievable level of sophistication, particularly in its interactions with other agents (Lesser 1991;1998). We believe that a general control architecture, responsible for both the planning for the achievement of temporally constrained goals of varying worth, and the sequencing of actions local to the agent that have resource requirements, can provide a robust and reusable platform on which to build high level reasoning compo...