Communication is an important aspect of teamwork, both in human teams and in multi-agent teams. One of the most vital roles for communication is for information exchange, such as for performing team situation assessment. In previous work, we have shown how agents can automatically generate messages for proactive information exchange by inferring relevance based on analysis of requirements of tasks other team members are involved in. However, for the sake of efficiency, it is important to restrict message passing to cases where one agent is reasonably sure another agent does not already believe the information about to be sent. This requires being able to infer and track the belief states of other members on the team. One way this can be done is by reasoning about commonly observable information in the environment. In this paper we introduce a formal framework for reasoning about observability, along with practical algorithms for updating beliefs about other agents’ beliefs. We demonstrate the utility of this approach to intelligently filtering communication in a synthetic task domain.
Published Date: May 2004
Registration: ISBN 978-1-57735-201-3
Copyright: Published by The AAAI Press, Menlo Park, California.