Coordinating activity in knowledge-intensive dynamic systems

We consider the problem of coordinating activity in knowledge-intensive dynamic systems (KIDS) - large-scale, multiagent systems in which agents with significant individual capabilities work together to accomplish complex, knowledge-intensive tasks. Given the time pressure and resource limitations under which a KIDS typically operates, the establishment of plans and schedules can significantly improve organizational performance. However, there are several complicating factors: (1) there is diversity and novelty in the structure of processes that must be executed over time, requiring tight coupling of action selection with resource allocation, (2) processes are unpredictable in their outcomes and require continual dynamic adjustment and revision, (3) the collective capabilities of the agents of a KIDS are its primary asset and, to minimize future resource limitations, task allocation should consider the side effects of acquired expertise, and (4) KIDS are large-scale enterprises, requiring an ability to effectively distribute decision-making. Our previous research has developed constraint-based search techniques for continuous, dynamic scheduling that have been successfully applied to complex, large-scale transportation and manufacturing domains. We outline current work aimed at extending these models to address the above issues and provide an effective basis for managing KIDS.