PARTIAL GLOBAL PLANNING - A COORDINATION FRAMEWORK FOR DISTRIBUTED HYPOTHESIS FORMATION

For distributed sensor network applications, a practical approach to generating complete interpretations from distributed data must coordinate how separate, concurrently running systems form, exchange, and fuse their individual hypotheses to form consistent interpretations. Partial global planning provides a framework for coordinating multiple AI systems that are cooperating in a distributed sensor network. By combining a variety of coordination techniques into a single, unifying framework, partial global planning enables separate AI systems to reason about their roles and responsibilities as part of group problem solving, and to modify their planned processing and communication actions to act as a more coherent team. Partial global planning is uniquely suited for coordinating systems that are working in continuous, dynamic, and unpredictable domains because it interleaves coordination with action and allows systems to make effective decisions despite incomplete and possibly obsolete information about network activity. The authors have implemented and extensively evaluated partial global planning in a simulated vehicle monitoring application, and have identified promising extensions to their framework.