How can AI agents best manage air traffic in bad weather?
Pathfinders in the Sky: Formal Decision-Making Models for Collaborative Air Traffic Control in Convective Weather
This paper explores how to model the decision-making process of using "pathfinder" aircraft to determine if airspace closed due to weather can be reopened. It uses a Markov chain to model the process of selecting a pathfinder, sending it out, and deciding whether to reopen airspace based on its findings. A stylized model incorporating game theory concepts is used to simulate how individual aircraft decide whether to accept the pathfinder role, considering potential costs and rewards. The research also analyzes the risk of all aircraft refusing to be pathfinders.
Relevant to LLM-based multi-agent systems, the research shows how sequential decision-making can be modeled in multi-agent environments. The stylized utility model could be implemented and expanded using LLMs to simulate more nuanced agent behavior, considering individual flight characteristics, risk tolerance, and even communication between agents (aircraft). The Markov chain provides a framework for tracking the state of the multi-agent system as individual agents make decisions, and the overall system evolves. This research offers a starting point for developing more sophisticated LLM-based simulations of complex multi-agent systems like air traffic control.