  Multi-agent and multi-robot systems (MRS) often rely on direct communication
for information sharing. This work explores an alternative approach inspired by
eavesdropping mechanisms in nature that involves casual observation of agent
interactions to enhance decentralized knowledge dissemination. We achieve this
through a novel IKT-BT framework tailored for a behavior-based MRS,
encapsulating knowledge and control actions in Behavior Trees (BT). We present
two new BT-based modalities - eavesdrop-update (EU) and eavesdrop-buffer-update
(EBU) - incorporating unique eavesdropping strategies and efficient episodic
memory management suited for resource-limited swarm robots. We theoretically
analyze the IKT-BT framework for an MRS and validate the performance of the
proposed modalities through extensive experiments simulating a search and
rescue mission. Our results reveal improvements in both global mission
performance outcomes and agent-level knowledge dissemination with a reduced
need for direct communication.
