Work

$EvoAl^{2048}$

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2408.16780

Authors: Berger, Bernhard J., Plump, Christina, Drechsler, Rolf

Institutions: University of Rostock, Hamburg University of Technology, German Research Centre for Artificial Intelligence, University of Bremen

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4403556072

Cites:

Cited by:

Related to: 10