Multi-Agent Negotiation for Human-Centric Vehicle Configuration (Sim-DSE)

Autonomous vehicles handle the road, but not the room. When one passenger wants to sleep (dim lights, silence) while another works (bright lights, conference call), how should systems resolve passenger needs and preference? Multi-Agent Negotiation Framework I architected a system where each occupant is represented by an AI agent that advocates for their preferences. Using Large Language Models (LLMs), these agents negotiate optimal cabin configurations through natural language reasoning, for example: "Lower volume to 15% for sleeping Passenger A, maintain 40% for Passenger B's call" Three-Axis Design Space: • User Action: Occupant behaviors (sleeping, reading, working) • System Reaction: Vehicle responses (adjust seat, modify climate) • Reasoning: AI-generated contextual justifications Validation Strategy I'm conducting dual validation: (1) Simulation experiments testing decision-making across realistic scenarios, and (2) Human alignment surveys where participants judge whether agent reasoning matches human expectations. Preliminary results show agents propose solutions via consensus. Implementation I am building a physical prototype integrating the Model Context Protocol (MCP) and Home Assistant to control real hardware (e.g.,lighting). This hardware-software integration validates the framework beyond simulation. Currently brainstorming and exploring Reinforcement Learning techniques such as Reinforcement Learning from Human Feedback (RLHF) to fine-tune agent behavior based on online survey data.