# Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments

arXiv: https://arxiv.org/abs/1710.01447 · HTML: https://mlsanity.com/paper/1710.01447/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2017.

## Built on by (most cited first, 22 of 22)

- [Distributed Heuristic Multi-Agent Path Finding with Communication](https://mlsanity.com/paper/2106.11365/) (2021)
- [Guidance Graph Optimization for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2402.01446/) (2024)
- [Graph-Based Multi-Robot Path Finding and Planning](https://mlsanity.com/paper/2206.11319/) (2022)
- [Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding](https://mlsanity.com/paper/2403.07559/) (2024)
- [Advancing MAPF Toward the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)](https://mlsanity.com/paper/2503.04798/) (2025)
- [Exact Algorithms and Lowerbounds for Multiagent Pathfinding: Power of Treelike Topology](https://mlsanity.com/paper/2312.09646/) (2023)
- [HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding](https://mlsanity.com/paper/2402.15546/) (2024)
- [MGCBS: An Optimal and Efficient Algorithm for Solving Multi-Goal Multi-Agent Path Finding Problem](https://mlsanity.com/paper/2404.19518/) (2024)
- [A Competitive Analysis of Online Multi-Agent Path Finding](https://mlsanity.com/paper/2106.11454/) (2021)
- [The Study of Highway for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2304.04217/) (2023)
- [Optimal and Bounded Suboptimal Any-Angle Multi-agent Pathfinding](https://mlsanity.com/paper/2404.16379/) (2024)
- [Robust Multi-Agent Pickup and Delivery with Delays](https://mlsanity.com/paper/2303.17422/) (2023)
- [QD-MAPPER: A Quality Diversity Framework to Automatically Evaluate Multi-Agent Path Finding Algorithms in Diverse Maps](https://mlsanity.com/paper/2409.06888/) (2024)
- [MFC-EQ: Mean-Field Control with Envelope Q-Learning for Moving Decentralized Agents in Formation](https://mlsanity.com/paper/2410.12062/) (2024)
- [LLMDR: LLM-Driven Deadlock Detection and Resolution in Multi-Agent Pathfinding](https://mlsanity.com/paper/2503.00717/) (2025)
- [WinkTPG: An Execution Framework for Multi-Agent Path Finding Using Temporal Reasoning](https://mlsanity.com/paper/2508.01495/) (2025)
- [Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2602.23468/) (2026)
- [An Efficient Approach to the Online Multi-Agent Path Finding Problem by Using Sustainable Information](https://mlsanity.com/paper/2301.04446/) (2023)
- [Algorithm Selection for Optimal Multi-Agent Path Finding via Graph Embedding](https://mlsanity.com/paper/2406.10827/) (2024)
- [Multi-Robot Reliable Navigation in Uncertain Topological Environments with Graph Attention Networks](https://mlsanity.com/paper/2411.16134/) (2024)
- [Enhancing Lifelong Multi-Agent Path-finding by Using Artificial Potential Fields](https://mlsanity.com/paper/2505.22753/) (2025)
- [Analyzing Planner Design Trade-offs for MAPF under ADG-based Realistic Execution](https://mlsanity.com/paper/2512.09736/) (2025)

## Abstract (arXiv)

Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence, where one needs to find collision-free paths for agents with given start and goal locations. In video games, agents of different types often form teams. In this paper, we demonstrate the usefulness of MAPF algorithms from artificial intelligence for moving such non-homogeneous teams in congested video game environments.