# EvoAl2048

arXiv: https://arxiv.org/abs/2408.16780 · HTML: https://mlsanity.com/paper/2408.16780/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 15 Aug 2024.

## Abstract (arXiv)

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.