Distributed Multi-agent Reasoning with Layered Context Modeling and Priority

Context awareness service is one of the key features in ubiquitous computing system. In heterogeneous pervasive computing system, effective context modeling and reasoning are important to enable the collaboration and distributed reasoning among the agents. The effectiveness of the previous approaches for distributed reasoning significantly degrades when a large number of agents are involved. In order to solve this problem we propose a layered context model facilitating distributed reasoning. We also propose an approach for grouping the agents based on their importance. The proposed approaches can reduce the amount of resource required for updating the context information, and allow the system to more stably and flexibly adapt to the changing environment. The performance of the proposed scheme is verified by computer simulation, and it shows that the trust of reasoning outcome is greatly enhanced with less resource than the earlier scheme.