Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet

• URL: http://arxiv.org/abs/2401.03630v2
• Date: Fri, 9 Feb 2024 17:48:19 GMT
• Title: Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet
• Authors: Weizhe Chen, Sven Koenig, Bistra Dilkina
• Abstract summary: We focus on the problem of multi-agent path finding (MAPF), which is also known as multi-robot route planning. We show the motivating success on an empty room map without obstacles, then the failure to plan on the harder room map and maze map of the standard MAPF benchmark.
• Score: 31.253063077167617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the explosive influence caused by the success of large language models (LLM) like ChatGPT and GPT-4, there has been an extensive amount of recent work showing that foundation models can be used to solve a large variety of tasks. However, there is very limited work that shares insights on multi-agent planning. Multi-agent planning is different from other domains by combining the difficulty of multi-agent coordination and planning, and making it hard to leverage external tools to facilitate the reasoning needed. In this paper, we focus on the problem of multi-agent path finding (MAPF), which is also known as multi-robot route planning, and study the performance of solving MAPF with LLMs. We first show the motivating success on an empty room map without obstacles, then the failure to plan on the harder room map and maze map of the standard MAPF benchmark. We present our position on why directly solving MAPF with LLMs has not been successful yet, and we use various experiments to support our hypothesis. Based on our results, we discussed how researchers with different backgrounds could help with this problem from different perspectives.

Related papers

• MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale [46.35418789518417]
  Multi-agent pathfinding is a challenging computational problem that typically requires to find collision-free paths for multiple agents in a shared environment. We have created a foundation model for the MAPF problems called MAPF-GPT. Using imitation learning, we have trained a policy on a set of sub-optimal expert trajectories that can generate actions in conditions of partial observability. We show that MAPF-GPT notably outperforms the current best-performing learnable-MAPF solvers on a diverse range of problem instances.
  arXiv  Detail & Related papers  (2024-08-29T12:55:10Z)
• Multi-LLM QA with Embodied Exploration [55.581423861790945]
  We investigate the use of Multi-Embodied LLM Explorers (MELE) for question-answering in an unknown environment. Multiple LLM-based agents independently explore and then answer queries about a household environment. We analyze different aggregation methods to generate a single, final answer for each query.
  arXiv  Detail & Related papers  (2024-06-16T12:46:40Z)
• UCB-driven Utility Function Search for Multi-objective Reinforcement Learning [75.11267478778295]
  In Multi-objective Reinforcement Learning (MORL) agents are tasked with optimising decision-making behaviours. We focus on the case of linear utility functions parameterised by weight vectors w. We introduce a method based on Upper Confidence Bound to efficiently search for the most promising weight vectors during different stages of the learning process.
  arXiv  Detail & Related papers  (2024-05-01T09:34:42Z)
• CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving [9.446546965008249]
  We propose a collaborative multi-agent, multi-reasoning-path (CoMM) prompting framework. Specifically, we prompt LLMs to play different roles in a problem-solving team, and encourage different role-play agents to collaboratively solve the target task. Empirical results demonstrate the effectiveness of the proposed methods on two college-level science problems.
  arXiv  Detail & Related papers  (2024-04-26T23:29:12Z)
• Enhancing the General Agent Capabilities of Low-Parameter LLMs through Tuning and Multi-Branch Reasoning [56.82041895921434]
  Open-source pre-trained Large Language Models (LLMs) exhibit strong language understanding and generation capabilities. When used as agents for dealing with complex problems in the real world, their performance is far inferior to large commercial models such as ChatGPT and GPT-4.
  arXiv  Detail & Related papers  (2024-03-29T03:48:12Z)
• Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding [49.730902939565986]
  Multi-Agent Pathfinding problem involves finding a set of conflict-free paths for a group of agents confined to a graph. In this study, we focus on the decentralized MAPF setting, where the agents may observe the other agents only locally. We propose a decentralized multi-agent Monte Carlo Tree Search (MCTS) method for MAPF tasks.
  arXiv  Detail & Related papers  (2023-12-26T06:57:22Z)
• Monte-Carlo Tree Search for Multi-Agent Pathfinding: Preliminary Results [60.4817465598352]
  We introduce an original variant of Monte-Carlo Tree Search (MCTS) tailored to multi-agent pathfinding. Specifically, we use individual paths to assist the agents with the the goal-reaching behavior. We also use a dedicated decomposition technique to reduce the branching factor of the tree search procedure.
  arXiv  Detail & Related papers  (2023-07-25T12:33:53Z)
• Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate [85.3444184685235]
  We propose a Multi-Agent Debate (MAD) framework, in which multiple agents express their arguments in the state of "tit for tat" and a judge manages the debate process to obtain a final solution. Our framework encourages divergent thinking in LLMs which would be helpful for tasks that require deep levels of contemplation.
  arXiv  Detail & Related papers  (2023-05-30T15:25:45Z)
• Subdimensional Expansion Using Attention-Based Learning For Multi-Agent Path Finding [9.2127262112464]
  Multi-Agent Path Finding (MAPF) finds conflict-free paths for multiple agents from their respective start to goal locations. We develop a novel multi-agent planner called LM* by integrating this learning-based single-agent planner with M*. Our results show that for both "seen" and "unseen" maps, in comparison with M*, LM* has fewer conflicts to be resolved and thus, runs faster and enjoys higher success rates.
  arXiv  Detail & Related papers  (2021-09-29T20:01:04Z)
• Compilation-based Solvers for Multi-Agent Path Finding: a Survey, Discussion, and Future Opportunities [7.766921168069532]
  We show the lessons learned from past developments and current trends in the topic and discuss its wider impact. Two major approaches to optimal MAPF solving include (1) dedicated search-based methods, which solve MAPF directly, and (2) compilation-based methods that reduce a MAPF instance to an instance in a different well established formalism.
  arXiv  Detail & Related papers  (2021-04-23T20:13:12Z)
• Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming [1.7132914341329848]
  The real-world applications of mMAPF require flexibility and explainability. This paper introduces a method for generating explanations for queries regarding the feasibility and optimality of solutions.
  arXiv  Detail & Related papers  (2020-08-08T18:34:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.