Preference elicitation and alignment among strategic agents in MOMARL

Develop mechanisms and learning frameworks for multi-objective multi-agent reinforcement learning in which agents concurrently learn their users’ utility functions and optimal policies despite misaligned incentives and strategic behaviour (including hiding or misrepresenting preferences). Specifically, design negotiation, communication, or social-contract protocols and accompanying algorithms that elicit and align preferences across agents and stakeholders in individual-utility settings, ensuring robust performance when agents may benefit from not sharing preferences openly.

Background

In single-agent settings, interactive preference elicitation can align policies with user trade-offs over objectives. In multi-agent settings with individual utilities, however, agents may have incentives not to reveal their preferences or may benefit from actively hiding them, complicating preference elicitation and coordination.

The paper highlights that interactive MOMARL—where agents simultaneously learn how to act and model users’ preferences—has not yet been explored. Addressing misalignment and strategic concealment calls for new protocols and algorithms (e.g., negotiations or social contracts) that can reliably elicit preferences and enable aligned multi-agent behaviour.