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In recent years, multi-agent Partially Observable Markov Decision Processes (POMDP) have emerged as a popular decision-theoretic framework for modeling and generating policies for the control of multi-agent teams. Teams controlled by multi-agent POMDPs can use communication to share observations and coordinate. Therefore, policies are needed to enable these teams to reason about communication. Previous work on generating communication policies for multi-agent POMDPs has focused on the question of when to communicate. In this paper, we address the question of what to communicate. We describe two paradigms for representing limitations on communication and present an algorithm that enables multi-agent teams to make execution-time decisions on how to effectively utilize available communication resources. [ABSTRACT FROM AUTHOR]

The market based approach is widely used to solve the problem of multirobot coordination. In this approach, communication and computation costs are key issues, but have not been carefully addressed by the different architectures in the literature. In this paper, we present a method to reduce these costs, by adding the capability to learn whether a task is worth offering up for auction and also whether it is worth bidding for the task, based on previous experience about successful and unsuccessful bids. We show that the method significantly decreases communication and computation costs, while maintaining good overall performance of the team. [ABSTRACT FROM AUTHOR]