Fischer/etal/2020a: Solving Abstract Reasoning Tasks with Grammatical Evolution

Bibtype Inproceedings
Bibkey Fischer/etal/2020a
Author Fischer, Raphael and Jakobs, Matthias and Mücke, Sascha and Morik, Katharina
Ls8autor Fischer, Raphael
Jakobs, Matthias
Morik, Katharina
Mücke, Sascha
Editor Trabold, Daniel and Welke, Pascal and Piatkowski, Nico
Title Solving Abstract Reasoning Tasks with Grammatical Evolution
Booktitle Proceedings of the {LWDA} 2020 Workshops: {KDML}, {FGWM}, {FGWI-BIA}, and {FGDB}
Series {CEUR} Workshop Proceedings
Pages 6--10
Abstract The Abstraction and Reasoning Corpus (ARC) comprising image-based logical reasoning tasks is intended to serve as a benchmark for measuring intelligence. Solving these tasks is very difficult for offthe-shelf ML methods due to their diversity and low amount of training data. We here present our approach, which solves tasks via grammatical evolution on a domain-specific language for image transformations. With this approach, we successfully participated in an online challenge, scoring among the top 4% out of 900 participants.
Month 09
Year 2020
Projekt ML2R
Url https://www.ifd2020.nrw/wp-content/uploads/2020/09/LWDA2020_Proceedings.pdf

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