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Achieving knowledge sharing within an artificial swarm system could lead tosignificant development in autonomous multiagent and robotic systems researchand realize collective intelligence. However, this is difficult to achievesince there is no generic framework to transfer skills between agents otherthan a query-response-based approach. Moreover, natural living systems have a"forgetfulness" property for everything they learn. Analyzing such ephemeralnature (temporal memory properties of new knowledge gained) in artificialsystems has never been studied in the literature. We propose a behaviortree-based framework to realize a query-response mechanism for transferringskills encoded as the condition-action control sub-flow of that portion of theknowledge between agents to fill this gap. We simulate a multiagent group withdifferent initial knowledge on a foraging mission. While performing basicoperations, each robot queries other robots to respond to an unknown condition.The responding robot shares the control actions by sharing a portion of thebehavior tree that addresses the queries. Specifically, we investigate theephemeral nature of the new knowledge gained through such a framework, wherethe knowledge gained by the agent is either limited due to memory or isforgotten over time. Our investigations show that knowledge growsproportionally with the duration of remembrance, which is trivial. However, wefound minimal impact on knowledge growth due to memory. We compare these casesagainst a baseline that involved full knowledge pre-coded on all agents. Wefound that knowledge-sharing strived to match the baseline condition by sharingand achieving knowledge growth as a collective system.

A study on the ephemeral nature of knowledge shared within multiagent systems