$EvoAl^{2048}$

• URL: http://arxiv.org/abs/2408.16780v1
• Date: Thu, 15 Aug 2024 21:06:18 GMT
• Title: $EvoAl^{2048}$
• Authors: Bernhard J. Berger, Christina Plump, Rolf Drechsler,
• Abstract summary: We report on applying a model-driven optimisation to search for an interpretable and explainable policy. This paper describes a solution to the GECCO'24 Interpretable Control Competition using the open-source software EvoAl.
• Score: 2.5526759890882764
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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