How can fuzzy agents optimize airport robot fleets?
Simulation of Autonomous Industrial Vehicle Fleet Using Fuzzy Agents: Application to Task Allocation and Battery Charge Management
This paper explores optimizing task allocation and battery management for a fleet of autonomous industrial vehicles (AIVs), specifically baggage robots at an airport, using a fuzzy multi-agent system. The system simulates various task allocation strategies (random, FIFO, availability-based) and incorporates fuzzy logic to handle uncertainties in factors like baggage arrival flow, AIV availability, and energy consumption. Key improvements include incorporating fuzzy heuristics for selecting optimal recharging stations, adjusting recharge rates based on baggage flow, and dynamically regulating AIV speed. This allows for a more efficient and robust system compared to traditional methods.
For LLM-based multi-agent systems, this research demonstrates the value of fuzzy logic for handling real-world uncertainty and the importance of context-aware decision-making. The heuristics presented, like dynamically adjusting agent behavior (speed, charging) based on system state (baggage flow, station availability), can inspire similar strategies in LLM-agent collaborations where resource management and task prioritization are critical. The focus on decentralized decision-making also aligns with the trend of distributing agency in complex LLM-agent applications.