
Multi-Agent Path Finding (MAPF) is a well studied problem with many existing optimal algorithms capable of solving a wide variety of instances, each with its own strengths and weaknesses. 
While for some instances the fastest algorithm can be easily determined, not enough is known about their performance to predict the fastest algorithm for every MAPF instance, or what makes some instances more difficult than others. There is no clear answer for which features dominate the hardness of MAPF instances. 
In this work, we study how betweenness centrality affects the empirical difficulty of MAPF instances. To that end, we benchmark the largest and most complete optimal MAPF algorithm portfolio to date. We analyze the algorithms' performance independently and as part of the portfolio, and discuss how betweenness centrality can be used to improve estimations of algorithm performance on a given instance of MAPF.

Betweenness Centrality in Multi-Agent Path Finding

Solving the Multi-Agent Path Finding (MAPF) problem optimally is known to be NP-Hard for both make-span and total arrival time minimization. While many algorithms have been developed to solve MAPF problems, there is no dominating optimal MAPF algorithm that works well in all types of problems and no standard guidelines for when to use which algorithm. In this work, we develop the deep convolutional network MAPFAST (Multi-Agent Path Finding Algorithm SelecTor), which takes a MAPF problem instance and attempts to select the fastest algorithm to use from a portfolio of algorithms. We improve the performance of our model by including single-agent shortest paths in the instance embedding given to our model and by utilizing supplemental loss functions in addition to a classification loss. We evaluate our model on a large and diverse dataset of MAPF instances, showing that it outperforms all individual algorithms in its portfolio as well as the state-of-the-art optimal MAPF algorithm selector. We also provide an analysis of algorithm behavior in our dataset to gain a deeper understanding of optimal MAPF algorithms' strengths and weaknesses to help other researchers leverage different heuristics in algorithm designs.

MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings


**Session Chair:** <br><br> Roberta Calegari

Oral Session 1A3-1

technical paper

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

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AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

S4: Multiagent Planning and Scheduling 1

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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Eric Ewing

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SHORT BIO

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Betweenness Centrality in Multi-Agent Path Finding

MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings

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