Learning in the Broker Agent

Service matching is one of the crucial elements in the success of large, open agent systems. While finding “perfect” matches is always desirable, it is not always possible. The capabilities of an agent may change over time; some agents may be unwilling to, or unable to communicate their capabilities at the right level of details. The solution we propose is to have the broker agent dynamically refine the agent's capability model and to conduct performance rating. The agent capability model will be updated using the information from the consumer agent feedback, capability querying, etc. The update process is based on a concept of “dynamic weight sum system”, as well as based on the local distribution of the agent services. We assume that the agents in the system share a common domain ontology that will be represented in DAML+OIL, and the agent capabilities will be described using DAML-S.