2025/02/26 by Hung-Yuan Su, Huangyuan Su, Su, Huangyuan +10 · 1 voice · 1 citation
Computer Science · #AI-based Problem Solving and Planning #Constraint Satisfaction and Optimization #Multimodal Machine Learning Applications #cs.AI #cs.MA
paper · pdf · doi:10.48550/arxiv.2502.18822
In this project, our goal is to determine how to leverage the world-knowledge of pretrained large language models for efficient and robust learning in multiagent decision making. We examine this in a taxi routing and assignment problem where agents must decide how to best pick up passengers in order to minimize overall waiting time. While this problem is situated on a graphical road network, we show that with the proper prompting zero-shot performance is quite strong on this task. Furthermore, with limited fine-tuning along with the one-at-a-time rollout algorithm for look ahead, LLMs can out-compete existing approaches with 50 times fewer environmental interactions. We also explore the benefits of various linguistic prompting approaches and show that including certain easy-to-compute information in the prompt significantly improves performance. Finally, we highlight the LLM's built-in semantic understanding, showing its ability to adapt to environmental factors through simple prompts.