Increasing Coherence in a Distributed Problem-Solving Network

Globally coherent behavior is the holy grail of distributed problem solving network research. Obtaining coherent network activity without sacrificing node autonomy and network flexibility places severe demands on the local control component of each node. We introduce new mechanisms that allow a node to compute an abstracted, high-level description of its local state which it then uses to formulate multi-step plans. Not only do these mechanisms significantly improve local problem solving performance, but they also enable nodes to make dynamic refinements to their long-term network organisation knowledge. The coordination decisions made by nodes are thus increasingly responsive to changes in network activity as problem solving progresses. We provide experimental results indicating that these new mechanisms improve the internal control decisions of a node, reduce the communication requirements of the network, and improve network coherence. We believe that these mechanisms would also be useful for control in centralised multi-level blackboard-based problem solving systems. I . I N T R O D U C T I O N Achieving global coherence in cooperative distributed problem solving networks (DPSNs) is a major problem [4,13]. In a DPSN, each node is an intelligent semiautonomous problem solving agent that determines its own behavior based on its perception of network activities. Global coherence means that the activities of the nodes should appear to make sense given overall network goals. Nodes should avoid unnecessarily duplicating the work of others, sitting idle while others are swamped with work, or transmitting information that will not improve overall network performance. Because network coordination must be decentralised to improve reliability and responsiveness, the amount of global coherence in the network is dependent on the degree to which each node makes coherent decisions based on its local view of network problem solving activities. This research was sponsored, in part, by the National Science Foundation under Grant MCS-S300230 and by the Defense Advanced Research Projects Agency (DOD), monitored by the Office of Naval Research under Contract NR049-041. At any given time, a node will rank its pending tasks based on how it believes each will improve network problem solving. A decision by the node to execute the top ranked task is therefore more or less coherent depending on how highly ranked the task would have been if the node had a completely global view of network problem solving. Full global coherence requires that each node have a complete and accurate view of the past, present, and intended future activities of all other nodes. If this is done by globally predefining a coordinated multi-agent plan at network creation, the network will be inflexible to changing problem solving situations and network characteristics. Alternatively, having nodes broadcast all state changes and future intentions is infeasible due to bandwidth limitations and channel delays. Therefore, we have no practical means to insure full global coherence. The functionally accurate, cooperative approach to distributed problem solving develops a framework in which network goals can be achieved with only partial global coherence [13]. However, since partial coherence wastes resources and degrades performance, we have been developing mechanisms which increase coherence without significant additional communication costs. Our previous work toward this end developed a decentralised approach to network coordination in which each node is guided by a high-level strategic plan for cooperation among the nodes in the network [3]. This strategic plan, represented as a network organisational structure, specifies in a general way the communication and control relationships among the nodes. The organisational structure increases the likelihood that nodes will be coherent in their behavior by predefining a limited range of options available to a node. Network flexibility is maintained by not limiting these options too tightly. Sophisticated local control plays a key part in this approach because decisions about which of these options to pursue must be based on short-term information about the current situation. In this paper, we describe new mechanisms that allow a node to refine its perception of the role it currently plays in the organization. This refined view is achieved by providing each node with the ability to reason about its current state of problem solving and to make predictions about its future actions. To accomplish this, these new