Increasing Coherence in a Distributed Problem-Solving Network

Abstract

Globally coherent behavior is the holy grail of distributed problern eolving network research. Obtaining coherent network activity without sacrificing node autonomy and network flexibility placea severe demanda on the local control component of each node. We introduce new mechanislD8 that allow a node to compute an abstracted, high-level description of its local state which it then usea to formulate multi-step plans. Not only do these mechanislD8 significantly improve local problern eolving performance, but they also enable nodes to make dynamic refinements to their long-term network organizaUon knowledge. The coordination decisions made by nodes are thus increasingly responsive to changes in network activity as problern solving progresses. We provide experimental results indicating that these new mechanislD8 improve the intemal control decisions of a node, reduce the communicaUon requirements of the network, and improve network coherence. We believe that these mechanislD8 would also be useful for control in centralized multi-level blackboard-based problern eolving systelD8.