# Birds of a Different Feather Flock Together: Exploring Opportunities and Challenges in Animal-Human-Machine Teaming

arXiv: https://arxiv.org/abs/2504.13973 · HTML: https://mlsanity.com/paper/2504.13973/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 84 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies](https://mlsanity.com/paper/2112.11471/) (2021)
- [A Survey of Ad Hoc Teamwork Research](https://mlsanity.com/paper/2202.10450/) (2022)
- [Evaluating the Robustness of Collaborative Agents](https://mlsanity.com/paper/2101.05507/) (2021)
- [Modeling Trust in Human-Robot Interaction: A Survey](https://mlsanity.com/paper/2011.04796/) (2020)
- [Towards Robotic Companions: Understanding Handler-Guide Dog Interactions for Informed Guide Dog Robot Design](https://mlsanity.com/paper/2402.06790/) (2024)
- [Exploring the Cost of Interruptions in Human-Robot Teaming](https://mlsanity.com/paper/2311.00785/) (2023)
- [Biological Robots: Perspectives on an Emerging Interdisciplinary Field](https://mlsanity.com/paper/2207.00880/) (2022)
- [Bootstrapping Linear Models for Fast Online Adaptation in Human-Agent Collaboration](https://mlsanity.com/paper/2404.10733/) (2024)

## Built on by (most cited first, 1 of 1)

- [Artificial Intelligent Disobedience: Rethinking the Agency of Our Artificial Teammates](https://mlsanity.com/paper/2506.22276/) (2025)

## Abstract (arXiv)

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.