Better together: Multi-Agent system teamwork

Abstract

While most present-day multi-agent system (MAS) deployments involve teams of agents within a single institution orchestrated by one or a few actors, some researchers are already beginning to appreciate the promise of more complex ecosystems consisting of hybrid human-agent-robot teams that operate across organizational and geographic boundaries. Importantly, effective teamwork does not emerge merely from making individual agents more capable or from accumulated experience working alongside other agents. Rather, success requires every essential ingredient to be present and planned for; failure requires only one to be missing. In addition to leveraging what is already known about team structure, heterogeneity/diversity, and team processes, research should also account for the diverse task types and forms of interdependence that make up sophisticated, performative joint activity in the real world. Considering the high-stake opportunities and risks of MAS, a group of researchers from around the world have formed the international Multi-Agent System Safety and Teamwork (MASST) initiative. Essays in this this AI Magazine special issue on Multi-Agent System Teamwork—and its companion special issue on Multi-Agent System Safety and Human-Centered Agent Interaction—have been selected from the proceedings of past MASST events. In this article, we outline some of the needed requirements for making MAS “better together.” Innovative research relating to these requirements is grouped under four headings: 1. enabling scalable teamwork; 2. assuring multi-agent alignment; 3. managing the nuanced dynamics of trust and delegation; 4. theory and design of “coordination-first” systems