Benchmarks for pathfinding search: Iron Harvest

Daniel Harabor, Ryan Hechenberger, Thomas Jahn

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


Pathfinding is a central topic in AI for games, with many approaches having been suggested. But comparing different algorithms is tricky, because design choices stem from different practical considerations; e.g., some pathfinding systems are grid-based, others rely on a navigation mesh or visibility graph and so on. Current benchmarks mirror this trend, focusing on one set of assumptions while ignoring the rest. In this work we present a new unified benchmark using data from the game Iron Harvest. For 35 different levels in the game we generate several complementary map representations (grid, mesh and obstacle-set) and we provide a common set of challenging instances. We describe and analyse the new benchmark and then compare several leading pathfinding algorithms that begin from different assumption sets. Our goal is to allow researchers and practitioners to better understand the relative strengths and weakness of competing techniques.
Original languageEnglish
Title of host publicationProceedings of the International Symposium on Combinatorial Search
EditorsLukás Chrpa, Alessandro Saetti
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages5
ISBN (Electronic)1577358732
Publication statusPublished - 2022
EventInternational Symposium on Combinatorial Search 2022 - Vienna, Austria
Duration: 21 Jul 202223 Jul 2022
Conference number: 15th

Publication series

NameFifteenth InternationalSymposium on Combinatorial Search
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)


ConferenceInternational Symposium on Combinatorial Search 2022
Abbreviated titleSOCS 2022
Internet address


  • Search In Goal-directed Problem Solving

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