Experience-Based Resource Description and Selection in Multiagent Information Retrieval

In this paper, we propose an agent-centric approach to resource description and selection in a multiagent informatioretrieval (IR). In the multiagent system, each agent learnfrom its experience through its interactions with otheagents their capabilities and qualifications. Based on a distributed ontology learning framework, our methodology allows an agent to profile other agents in a dynamic translation table and a neighborhood profile, which together heldetermine resource description and selection process. Further, we report on the experiments and results of the firsphase of our research, which focuses on the operational issues (e.g., real-time constraints, frequency of queries, number of threads, narrowness in ontology) on how the agenthandle queries collaboratively.