AI Agent Protocols and Human Preference

We explore research ideas focused on understanding human preferences within AI agent ecosystems that utilize standardized protocols like MCP and A2A. It explores three interconnected approaches: dynamically eliciting user preferences during task execution leveraging these protocols, eliciting user preferences regarding the agents' interaction styles when using these protocols, and inferring users' latent preferences from the interaction logs generated by protocol use. The research intends to use the work of Max Kleiman-Weiner on computational cognitive science as a theoretical foundation for modeling and understanding these preferences. Ultimately, the goal is to develop methods for creating AI agents that are better aligned with human values and interaction preferences within these structured environments.