$EvoAl^{2048}$
Neural and Evolutionary Computing
2024-09-02 v1 Artificial Intelligence
Abstract
As AI solutions enter safety-critical products, the explainability and interpretability of solutions generated by AI products become increasingly important. In the long term, such explanations are the key to gaining users' acceptance of AI-based systems' decisions. We report on applying a model-driven-based optimisation to search for an interpretable and explainable policy that solves the game 2048. This paper describes a solution to the GECCO'24 Interpretable Control Competition using the open-source software EvoAl. We aimed to develop an approach for creating interpretable policies that are easy to adapt to new ideas.
Cite
@article{arxiv.2408.16780,
title = {$EvoAl^{2048}$},
author = {Bernhard J. Berger and Christina Plump and Rolf Drechsler},
journal= {arXiv preprint arXiv:2408.16780},
year = {2024}
}
Comments
2 pages, GECCO'24 competition entry