Learning Communication in Interactive Dynamic Influence Diagrams

Communication is one of central activities in multiagent systems. It enables the knowledge sharing among multiple agents and improves the planning quality in a long run. In this paper, we study communication decision problems in the framework of interactive dynamic influence diagrams~(I-DIDs). I-DIDs are recognized probabilistic graphical models for sequential decision making in uncertain multiagent settings. We extend the representation to explicitly model communication actions as well as their relations to other variables in the domain. The challenging work is on developing an incentive mechanism that drives level 0 agents to learn communication while they act alone in a dynamic environment. We present solutions to the new model and show meaningful communication strategies in a multiagent problem domain.