An adaptive adjusting mechanism for agent distributed blackboard architecture

Abstract Distributed blackboard is one of the popular agent communication architectures. However, in current agent systems, the distributed blackboard architecture is kept fixed after its initial setting, which may influence the system performance when network topology or agent cooperation relations are changed during operation. To solve the problem, this paper presents a novel mechanism for adjusting agent communication architecture. Based on graph theory, this mechanism provides a way to adjust the distributed blackboard architecture. The adjustment made to the architecture kept its validity, and the adjusted architecture outperforms the initial one in new network topology or agents cooperation relations, which are proved by the Mobile Ambients Calculus analysis and the simulation experiments. Therefore, the adjusting mechanism presented here can achieve the adaptation of the agent communication architecture to the changes of the network topology and agent cooperation relations.