---
title: "Connect Multi-Agent Path Finding: Generation and Visualization"
authors:
  - "Arthur Queffelec"
  - "Ocan Sankur"
  - "Francois Schwarzentruber"
doi: "10.24963/ijcai.2021/714"
url: "https://ijcai.org/proceedings/2021/714"
pdf: "https://ijcai.org/proceedings/2021/0714.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "5008-5011"
track: "Demo Track"
keywords:
  - "Multi-agent Systems: General"
  - "Planning and Scheduling: General"
---

# Connect Multi-Agent Path Finding: Generation and Visualization

Arthur Queffelec, Ocan Sankur, Francois Schwarzentruber

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 5008-5011. DOI: [10.24963/ijcai.2021/714](https://doi.org/10.24963/ijcai.2021/714)

## Abstract

We present a generic tool to visualize missions of the Connected Multi-Agent Path Finding (CMAPF) problem.  This problem is a variant of MAPF which requires a group of agents to navigate from an initial configuration to a goal configuration while maintaining connection. The user can create an instance of CMAPF and can play the generated plan. Any algorithm for CMAPF can be plugged into the tool.