Algorithm selection for optimal multi-agent pathfinding

Omri Kaduri, Eli Boyarski, Roni Stern

Research output: Contribution to journalConference articlepeer-review


The challenge of finding an optimal solution to a multi-agent path finding (MAPF) problem has attracted significant academic and industrial interest in recent years. While the problem is NP-Hard, modern optimal MAPF algorithms can scale to solve problems with hundreds of agents. Nevertheless, no single optimal MAPF algorithm dominates all benchmarks problems, and there are no clear, provable, guidelines for when each algorithm should be used. To address this, we present the first successful Algorithm Selection (AS) model for optimal MAPF. We propose two approaches for learning an AS model. The first approach uses a standard supervised learning algorithm with a set of handcrafted MAPF-specific features. The second approach, casts a MAPF problem to an image and applies a deep Convolutional Neural Network to classify it. We evaluate both approaches over a large dataset and show that using an AS model to select which algorithm to use for each instance results in solving more problems and in a shorter runtime compared to the state of the art.

Original languageEnglish
Pages (from-to)161-165
Number of pages5
JournalProceedings International Conference on Automated Planning and Scheduling, ICAPS
StatePublished - 29 May 2020
Event30th International Conference on Automated Planning and Scheduling, ICAPS 2020 - Nancy, France
Duration: 26 Oct 202030 Oct 2020

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management


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