$EvoAl^{2048}$

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-08-15 DOI:arxiv-2408.16780

Bernhard J. BergerUniversity of Rostock, Software Engineering Chair Rostock, GermanyHamburg University of Technology, Institute of Embedded Systems, Germany, Christina PlumpDFKI - Cyber-Physical Systems Bremen, Germany, Rolf DrechslerUniversity of Bremen, Departments of Mathematics and Computer ScienceDFKI - Cyber-Physical Systems Bremen, Germany

{"title":"$EvoAl^{2048}$","authors":"Bernhard J. BergerUniversity of Rostock, Software Engineering Chair Rostock, GermanyHamburg University of Technology, Institute of Embedded Systems, Germany, Christina PlumpDFKI - Cyber-Physical Systems Bremen, Germany, Rolf DrechslerUniversity of Bremen, Departments of Mathematics and Computer ScienceDFKI - Cyber-Physical Systems Bremen, Germany","doi":"arxiv-2408.16780","DOIUrl":null,"url":null,"abstract":"As AI solutions enter safety-critical products, the explainability and\ninterpretability of solutions generated by AI products become increasingly\nimportant. In the long term, such explanations are the key to gaining users'\nacceptance of AI-based systems' decisions. We report on applying a\nmodel-driven-based optimisation to search for an interpretable and explainable\npolicy that solves the game 2048. This paper describes a solution to the\nGECCO'24 Interpretable Control Competition using the open-source software\nEvoAl. We aimed to develop an approach for creating interpretable policies that\nare easy to adapt to new ideas.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":"38 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"$EvoAl^{2048}$\",\"authors\":\"Bernhard J. BergerUniversity of Rostock, Software Engineering Chair Rostock, GermanyHamburg University of Technology, Institute of Embedded Systems, Germany, Christina PlumpDFKI - Cyber-Physical Systems Bremen, Germany, Rolf DrechslerUniversity of Bremen, Departments of Mathematics and Computer ScienceDFKI - Cyber-Physical Systems Bremen, Germany\",\"doi\":\"arxiv-2408.16780\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As AI solutions enter safety-critical products, the explainability and\\ninterpretability of solutions generated by AI products become increasingly\\nimportant. In the long term, such explanations are the key to gaining users'\\nacceptance of AI-based systems' decisions. We report on applying a\\nmodel-driven-based optimisation to search for an interpretable and explainable\\npolicy that solves the game 2048. This paper describes a solution to the\\nGECCO'24 Interpretable Control Competition using the open-source software\\nEvoAl. We aimed to develop an approach for creating interpretable policies that\\nare easy to adapt to new ideas.\",\"PeriodicalId\":501347,\"journal\":{\"name\":\"arXiv - CS - Neural and Evolutionary Computing\",\"volume\":\"38 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Neural and Evolutionary Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.16780\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Neural and Evolutionary Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.16780","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

随着人工智能解决方案进入安全关键型产品，人工智能产品生成的解决方案的可解释性和可解读性变得越来越重要。从长远来看，这种解释是让用户接受人工智能系统决策的关键。我们报告了如何应用基于模型驱动的优化方法来寻找一种可解释和可解释的政策，以解决 2048 游戏。本文介绍了使用开源软件EvoAl为GECCO'24可解释控制竞赛（GECCO'24 Interpretable Control Competition）提供的解决方案。我们的目标是开发一种方法，用于创建易于适应新想法的可解释策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

$EvoAl^{2048}$

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

arXiv - CS - Neural and Evolutionary Computing

自引率

0.00%

发文量