AI as Collaborative Partner: Rethinking Human-AI Teaming for the Real World

Abstract

Much work in human-AI teaming today involves collaboration under fairly constrained settings. Humans supervise AI agents, who are relegated to following orders. The division of tasks is relatively superficial, with independent actions executed in parallel or with simple, linear dependencies between them. Communication is rigid and turn-based, with both parties speaking in complete, unambiguous sentences. However, collaboration in dynamic, real-world environments is rarely this straightforward. Team members adopt different roles as they continually adapt to an evolving situation, using efficient, timely communication to coordinate their actions. In this paper, we explore the technology requirements for AI agents to achieve this kind of collaboration and introduce COLLEAGUE, an agent framework designed to support adaptive, mixed-initiative interaction in dynamic domains.