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.

翻译：多智能体与多机器人系统（MRS）通常依赖直接通信进行信息共享。本研究探索了一种受自然界窃听机制启发的替代方法，通过偶然观察智能体间的交互来增强去中心化的知识传播。我们为此提出了一种新颖的IKT-BT框架，该框架专为基于行为的MRS设计，将知识与控制动作封装于行为树（BT）中。我们提出了两种基于BT的新模式——窃听-更新（EU）与窃听-缓冲-更新（EBU），融合了独特的窃听策略及适合资源受限群体机器人的高效情景记忆管理。我们从理论上分析了面向MRS的IKT-BT框架，并通过模拟搜索救援任务的大量实验验证了所提模式的性能。结果显示，该方法在减少直接通信需求的前提下，同时提升了全局任务绩效智能体级别的知识传播效果。