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Multi-agent and multi-robot systems (MRS) often rely on direct communicationfor information sharing. This work explores an alternative approach inspired byeavesdropping mechanisms in nature that involves casual observation of agentinteractions to enhance decentralized knowledge dissemination. We achieve thisthrough a novel IKT-BT framework tailored for a behavior-based MRS,encapsulating knowledge and control actions in Behavior Trees (BT). We presenttwo new BT-based modalities - eavesdrop-update (EU) and eavesdrop-buffer-update(EBU) - incorporating unique eavesdropping strategies and efficient episodicmemory management suited for resource-limited swarm robots. We theoreticallyanalyze the IKT-BT framework for an MRS and validate the performance of theproposed modalities through extensive experiments simulating a search andrescue mission. Our results reveal improvements in both global missionperformance outcomes and agent-level knowledge dissemination with a reducedneed for direct communication.

IKT-BT: Indirect Knowledge Transfer Behavior Tree Framework for Multi-Robot Systems Through Communication Eavesdropping