Task Decomposition Support to Reactive Scheduling

Abstract

This paper describes the development of an intelligent tasking model which has been designed to enable complex systems, human agents and software agents, to be tasked and controlled within a reactive work ow management paradigm. The task models exploit recent advances within the AI community in reactive control, scheduling and continuous execution. The Dynamic Execution Order Scheduler ( DEOS ) extends the current work ow paradigm to allow tasking in dynamic and uncertain environments by viewing the planning and scheduling tasks as being integrated and evolving entities. DEOS is being applied to the domains of Air Campaign Planning ( ACP ) and Intelligence, Surveillance and Reconnaissance ( ISR ) management. These are highly reactive domains in which new tasks and priorities are identified continuously and plans and schedules are generated and updated within a temporal and resource constrained setting.

This research is supported by DARPA Contract: DABT63-98-C-0069 “Intelligent Work ow for Collection Management” and Contract: F30602-97-1-0294 “Understanding and Exploiting Hierarchy”. The u.s. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are those of the author and should not be interpreted as necessarily representing official policies or endorsements, either express or implied, of DARPA , Rome Laboratory or the u.s. Government.