How can AI, humans, and animals best team up?
Birds of a Different Feather Flock Together: Exploring Opportunities and Challenges in Animal-Human-Machine Teaming
This paper explores Animal-Human-Machine (AHM) teams, a type of multi-agent system where animals, humans, and AI-powered machines collaborate. It proposes a framework for designing and optimizing these teams by considering the strengths and weaknesses of each member across individual capabilities (physical, informational, autonomy, planning, adaptability), interaction dynamics (communication, social intelligence, co-learning, trust, reliability), and resource constraints (interchangeability, expendability, vulnerability, training). The paper uses examples like security screening, search and rescue, and AI-assisted guide dogs to illustrate how this framework applies in real-world scenarios.
For LLM-based multi-agent systems, the paper's framework offers valuable considerations for designing effective collaboration between LLMs (as the machine agents), humans, and potentially other agents. Key aspects include understanding the limitations and capabilities of LLMs for different tasks, facilitating communication and shared planning between human and LLM agents, managing the autonomy and reliability of LLM agents within the system, and evaluating the resource costs associated with training and deploying LLMs. The interaction dimensions like shared mental models, trust, and transparency are especially crucial for successful human-LLM teaming.