Опубликована: Янв. 1, 2023
In this work We consider and discuss the problems which come with trying to explain human machine intelligence.How explainable artificial intelligence research is being carried out, pitfalls limitations of current approaches bigger question whether we need explanations for trusting inherently complex large intelligent systems, or not.
Язык: Английский
Proceedings of the Genetic and Evolutionary Computation Conference Companion, Год журнала: 2022, Номер unknown, С. 1757 - 1762
Опубликована: Июль 9, 2022
In the past decade, Explainable Artificial Intelligence (XAI) has attracted a great interest in research community, motivated by need for explanations critical AI applications. Some recent advances XAI are based on Evolutionary Computation (EC) techniques, such as Genetic Programming. We call this trend EC XAI. argue that full potential of methods not been fully exploited yet XAI, and community future efforts field. Likewise, we find there is growing concern regarding explanation population-based methods, i.e., their search process outcomes. While some attempts have done direction (although, most cases, those explicitly put context XAI), believe still several opportunities open questions that, principle, may promote safer broader adoption real-world within EC. position paper, briefly overview main results two above trends, suggest play major role achievement
Язык: Английский
Процитировано
36
Natural computing series, Год журнала: 2025, Номер unknown, С. 117 - 148
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Natural computing series, Год журнала: 2025, Номер unknown, С. 175 - 195
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Опубликована: Июль 15, 2023
tutorial Free Access Share on Exploratory Landscape Analysis Authors: Pascal Kerschke "Friedrich List" Faculty of Transport and Traffic Sciences, TU Dresden, Germany Center for Scalable Data Science (ScaDS.AI), Dresden/Leipzig, https://orcid.org/0000-0003-2862-1418Search about this author , Mike Preuss LIACS, University Leiden, Netherlands https://orcid.org/0000-0003-4681-1346Search Authors Info & Claims GECCO '23 Companion: Proceedings the Companion Conference Genetic Evolutionary ComputationJuly 2023Pages 990–1007https://doi.org/10.1145/3583133.3595058Published:24 July 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Alert added!This alert has been successfully added will be sent to:You notified whenever a record that you have chosen cited.To manage your preferences, click button below.Manage my Alert!Please log in to account Save BinderSave BinderCreate New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Язык: Английский
Процитировано
6
Expert Systems, Год журнала: 2023, Номер unknown
Опубликована: Ноя. 2, 2023
Abstract Explaining the decisions made by population‐based metaheuristics can often be considered difficult due to stochastic nature of mechanisms employed these optimisation methods. As industries continue adopt methods in areas that increasingly require end‐user input and confirmation, need explain internal being has grown. In this article, we present our approach extraction explanation supporting features using trajectory mining. This is achieved through application principal components analysis techniques identify new tracking population diversity changes post‐runtime. The algorithm search trajectories were generated solving a set benchmark problems with genetic univariate estimation distribution retaining all visited candidate solutions which then projected lower dimensional sub‐space. We also varied selection pressure placed on high fitness altering operators. Our results show metrics derived from sub‐space are capable capturing key learning steps how solution variable patterns function may captured component coefficients. A comparative study importance rankings surrogate model built same dataset was performed. both approaches identifying regarding interactions their influence complimentary fashion.
Язык: Английский
Процитировано
4
Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 326 - 341
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
0
2022 IEEE Congress on Evolutionary Computation (CEC), Год журнала: 2024, Номер unknown, С. 1 - 9
Опубликована: Июнь 30, 2024
Язык: Английский
Процитировано
0
Proceedings of the Genetic and Evolutionary Computation Conference Companion, Год журнала: 2024, Номер unknown, С. 135 - 138
Опубликована: Июль 14, 2024
Язык: Английский
Процитировано
0
arXiv (Cornell University), Год журнала: 2023, Номер unknown
Опубликована: Янв. 1, 2023
In black-box optimization, it is essential to understand why an algorithm instance works on a set of problem instances while failing others and provide explanations its behavior. We propose methodology for formulating footprint that consists are easy be solved difficult solved, instance. This behavior the further linked landscape properties which make some or challenging. The proposed uses meta-representations embed performance into same vector space. These obtained by training supervised machine learning regression model prediction applying explainability techniques assess importance features predictions. Next, deterministic clustering demonstrates using them captures across space detects regions poor good performance, together with explanation leading it.
Язык: Английский
Процитировано
0
Lecture notes in computer science, Год журнала: 2023, Номер unknown, С. 694 - 712
Опубликована: Янв. 1, 2023
Язык: Английский
Процитировано
0