Decentralised channel allocation and information sharing for teams of cooperative agents

In a wide range of emerging applications, from disaster management to intelligent sensor networks, teams of software agents can be deployed to effectively solve complex distributed problems. To achieve this, agents typically need to communicate locally sensed information to each other. However, in many settings, there are heavy constraints on the communication infrastructure, making it infeasible for every agent to broadcast all relevant information to everyone else. To address this challenge, we investigate how agents can make good local decisions about what information to send to a set of communication channels with limited bandwidths such that the overall system utility is maximised. Specifically, to solve this problem efficiently in large-scale systems with hundreds or thousands of agents, we develop a novel decentralised algorithm. This combines multi-agent learning techniques with fast decision-theoretic reasoning mechanisms that predict the impact a single agent has on the entire system. We show empirically that our algorithm consistently achieves 85% of a hypothetical centralised optimal strategy with full information, and that it significantly outperforms a number of baseline benchmarks (by up to 600%).