Considering operational issues for multiagent conceptual inferencing in a distributed information retrieval application

Our system, based on a multiagent framework called collaborative understanding of distributed knowledge (CUDK), is designed with the overall goal of balancing agents' conceptual learning and task accomplishment. The tradeoff between the two is that while conceptual learning allows an agent to improve its own concept base, it could be counter-productive: conceptual learning is time consuming and requires processing resources necessary for the agent to accomplish its tasks. In our current phase of research, we investigate the roles of resource and knowledge constraints, environmental factors (such as the frequency of queries), and learning mechanisms in a CUDK-based distributed information retrieval (DIR) application. In this application, an agent is motivated to learn about its neighbors' concept base so it can collaborate to satisfy queries that it cannot satisfy alone. Similarly, to conserve resources, an agent is motivated not to learn from neighbors that have been unhelpfu...

• Type: Journal
• Year: 2008
• Where: WIAS
• Authors: Leen-Kiat Soh
• Keywords: Concept Base, Conceptual, CUDK-based Distributed Information, WIAS 2008