From Following to Leading: Adaptive Collaboration and Influence for Multi-Agent Teaming

Abstract: Autonomous agents and robots are taking on increasingly collaborative roles alongside people, from self-driving vehicles that navigate roads alongside human drivers, to language-model agents that understand user intentions and execute tasks independently. In each of these settings, success is determined not only by an agent's individual task competence, but by its ability to work effectively with others whose preferences and strategies directly shape the outcome of the task. This talk explores collaborative intelligence, focusing on the requisite components to move from static, purely reactive agents to proactive collaborators that reason, adapt to, and shape the behaviors of their teammates. We introduce TALENTS, an ad hoc teamwork algorithm that analyzes teammate behavior and dynamically adapts its own policy to best suit them. To accomplish this, we first learn a latent strategy space from offline trajectory data via a variational autoencoder, cluster this space into discrete teammate types, and use a regret-minimization algorithm to infer and track which strategy a partner is following, allowing the cooperator to adapt online as the partner's behavior changes over the course of an episode. In both agent-agent evaluations and a 119 participant human-agent study in a modified version of the Overcooked-ai benchmark, we demonstrate that TALENTS outperforms existing baselines in both quantitative task reward as well as subjective measures of team fluency and trust. Finally, we extend beyond adaptation to examine proactive collaboration through the lens of multi-agent influence. Rather than treating a partner's strategy as fixed and simply best-responding to it, we investigate how an agent equipped with knowledge of how its teammate will respond to its actions can deliberately shape that learning process, motivating partners to shift toward more effective joint conventions. Together, these contributions establish several important algorithmic foundations needed to build autonomous agents that not only intelligently adapt to humans and other artificial teammates, but also actively help shape more effective collaboration. Committee: Katia Sycara (advisor) Jiaoyang Li Renos Zabounidis