Human-Machine Teaming for UAVs: An Experimentation Platform

Autor:	Moujtahid, Laila El, Gottipati, Sai Krishna, Mars, Clodéric, Taylor, Matthew E.
Rok vydání:	2023
Předmět:	Computer Science - Artificial Intelligence Computer Science - Human-Computer Interaction Computer Science - Machine Learning Computer Science - Multiagent Systems Statistics - Applications
Druh dokumentu:	Working Paper
Popis:	Full automation is often not achievable or desirable in critical systems with high-stakes decisions. Instead, human-AI teams can achieve better results. To research, develop, evaluate, and validate algorithms suited for such teaming, lightweight experimentation platforms that enable interactions between humans and multiple AI agents are necessary. However, there are limited examples of such platforms for defense environments. To address this gap, we present the Cogment human-machine teaming experimentation platform, which implements human-machine teaming (HMT) use cases that features heterogeneous multi-agent systems and can involve learning AI agents, static AI agents, and humans. It is built on the Cogment platform and has been used for academic research, including work presented at the ALA workshop at AAMAS this year [1]. With this platform, we hope to facilitate further research on human-machine teaming in critical systems and defense environments. Comment: 9 pages, 6 figures Presented at Conference on Artificial Intelligence for Defense (CAID) 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.11718 Zobrazit plný text záznamu View this record from Arxiv