Forming and searching content-based hierarchical agent clusters in distributed information retrieval systems

Abstract

The organization and collaborative protocols of agent societies are becoming increasingly important with the growing size of agent networks. Particularly, in a multi-agent-based content-sharing system, a flat, peer-to-peer (P2P) agent organization is not the most efficient organization for locating relevant agents for queries. This paper not only develops and analyzes a hierarchical agent group formation protocol to build a hybrid organization for large-scale content sharing systems, but proposes a context-aware distributed search algorithm to take advantage of such an organization as well. During the organization formation process, the agents manage their agent-view structures to form a hierarchical topology in an incremental fashion. The algorithm aims to place those agents with similar content in the same group. We evaluate the system performance based on TREC VLC 921 datasets. The results of the experiment demonstrate a significant increase in the cumulative recall ratio (CRR) measure compared to the flat agent organization and structure.