Balancing ontological and operational factors in refining multiagent neighborhoods

In this paper, we present our work balancing ontological and operational factors in building collaborations within multiagent neighborhoods. This innovation takes into account the desired level of performance, service priorities, and relaying of tasks to determine whether an agent should entertain ontological learning, which are more expensive but more rewarding in the long run, or carry out operational learning, which are less expensive and more rewarding in the short term. The domain of application is multiagent, distributed information retrieval, where agents, safe-guarding information or data resources, improve their local services by collaborating with others. Each agent is capable of providing query services to its users, and is equipped with an ontology defining the concepts that it knows and the associated documents. When collaborating, an agent needs to determine which agents to approach and how to approach them. Experiments show that with balanced profile-based reinforcement learning (operational) and inference-based ontological learning, agents reach desired level of performance while improving the neighborhood health and communication cost.