A Token-Based Approach to Sharing Beliefs in a Large Multiagent Team

Abstract

The performance of a cooperative team depends on the views that individual team members build of the environment in which they are operating. Teams with many vehicles and sensors generate a large amount of information from which to create those views. However, bandwidth limitations typically prevent exhaustive sharing of this information. As team size and information diversity grows, it becomes even harder to provide agents with needed information within bandwidth constraints, and it is impractical for members to maintain any detailed information for every team mate. Building on previous token-based algorithms, this chapter presents an approach for efficiently sharing information in large teams. The key distinction from previous work is that this approach models differences in how agents in the team value knowledge and certainty about features. By allowing the tokens passed through the network to passively estimate the value of certain types of information to regions of the network, it is possible to improve token routing through the use of local decision-theoretic models. We show that intelligent routing and stopping can increase the amount of locally useful information received by team members while making more efficient use of agents’ communication resources.