How can AI learn human preferences for better collaboration?
Controllable Complementarity: Subjective Preferences in Human-AI Collaboration
This research explores how to make AI agents better teammates by accounting for human preferences, specifically the desire for control over AI behavior. Researchers developed Behavior Shaping (BS), a reinforcement learning method that allows humans to directly influence AI actions. Experiments in a collaborative cooking game showed that humans preferred controllable AI partners and enjoyed working with them more, especially when the AI consistently adhered to the given control settings. These findings highlight the importance of designing controllable and predictable multi-agent systems that align with human expectations, potentially increasing human-AI team performance and user satisfaction. This is especially relevant for LLM-based multi-agent systems, where aligning with user intent and providing predictable responses are crucial for effective collaboration. The ability to control individual agent behavior through BS-like mechanisms could enhance user trust and allow for more fine-grained control over complex multi-agent interactions.