探索人-机-动物协同团队的设计，提升复杂任务的智能表现
Birds of a Different Feather Flock Together: Exploring Opportunities and Challenges in Animal-Human-Machine Teaming
- 构建多维度协同框架，发挥人、机、动物各自优势
- 通过安防、搜救、导盲三类场景验证协同效能
- 为混合智能系统研究提供新思路，适合跨学科研究者
人-机-动物（AHM）团队是一种混合智能系统，人类、人工智能驱动的机器与动物成员之间的互动可产生超越个体之和的独特能力。本文呼吁采用系统化方法研究AHM团队结构设计，以优化性能并克服应用中的局限性。通过引入一组描述AHM团队运作的维度，有效利用各成员优势并弥补个体短板。文章以安全筛查、搜索救援和导盲犬为例，说明此类团队如何应对复杂任务。最后提出该多维方法为研究更广泛的混合人机系统带来的开放性研究方向。
原文摘要 · Abstract (English)
Animal-Human-Machine (AHM) teams are a type of hybrid intelligence system wherein interactions between a human, AI-enabled machine, and animal members can result in unique capabilities greater than the sum of their parts. This paper calls for a systematic approach to studying the design of AHM team structures to optimize performance and overcome limitations in various applied settings. We consider the challenges and opportunities in investigating the synergistic potential of AHM team members by introducing a set of dimensions of AHM team functioning to effectively utilize each member's strengths while compensating for individual weaknesses. Using three representative examples of such teams -- security screening, search-and-rescue, and guide dogs -- the paper illustrates how AHM teams can tackle complex tasks. We conclude with open research directions that this multidimensional approach presents for studying hybrid human-AI systems beyond AHM teams.
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