What you need to know about a learning robot: Identifying the enabling architecture of complex systems

IF 2.1 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research Pub Date : 2024-09-28 DOI:10.1016/j.cogsys.2024.101286

Helen Beierling , Phillip Richter , Mara Brandt , Lutz Terfloth , Carsten Schulte , Heiko Wersing , Anna-Lisa Vollmer

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Abstract

Nowadays we deal with robots and AI more and more in our everyday life. However, their behavior is not always apparent to most lay users, especially in error situations. This can lead to misconceptions about the behavior of the technologies being used. This in turn can lead to misuse and rejection by users. Explanation, for example through transparency, can address these misconceptions. However, explaining the entire software or hardware would be confusing and overwhelming for users. Therefore, this paper focuses on the ‘enabling’ architecture. It describes those aspects of a robotic system that may need to be explained to enable someone to use the technology effectively. Furthermore, this paper deals with the ‘explanandum’, i.e. the corresponding misunderstandings or missing concepts of the enabling architecture that need to be clarified. Thus, we have developed and are presenting an approach to determine the ‘enabling’ architecture and the resulting ‘explanandum’ of complex technologies.

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关于学习型机器人，你需要知道什么？确定复杂系统的使能架构

如今，我们在日常生活中越来越多地与机器人和人工智能打交道。然而，对于大多数普通用户来说，它们的行为并不总是显而易见的，尤其是在出错的情况下。这可能导致人们对所使用技术的行为产生误解。这反过来又会导致用户的误用和排斥。解释（例如通过透明度）可以消除这些误解。然而，解释整个软件或硬件会让用户感到困惑和不知所措。因此，本文将重点放在 "使能 "架构上。它描述了机器人系统中可能需要解释的方面，以便让用户能够有效地使用该技术。此外，本文还涉及 "解释"，即需要澄清的相应误解或缺失的使能架构概念。因此，我们开发并提出了一种方法来确定复杂技术的 "使能 "架构和由此产生的 "解释"。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cognitive Systems Research 工程技术-计算机：人工智能

CiteScore

9.40

自引率

5.10%

发文量

审稿时长

>12 weeks

期刊介绍： Cognitive Systems Research is dedicated to the study of human-level cognition. As such, it welcomes papers which advance the understanding, design and applications of cognitive and intelligent systems, both natural and artificial. The journal brings together a broad community studying cognition in its many facets in vivo and in silico, across the developmental spectrum, focusing on individual capacities or on entire architectures. It aims to foster debate and integrate ideas, concepts, constructs, theories, models and techniques from across different disciplines and different perspectives on human-level cognition. The scope of interest includes the study of cognitive capacities and architectures - both brain-inspired and non-brain-inspired - and the application of cognitive systems to real-world problems as far as it offers insights relevant for the understanding of cognition. Cognitive Systems Research therefore welcomes mature and cutting-edge research approaching cognition from a systems-oriented perspective, both theoretical and empirically-informed, in the form of original manuscripts, short communications, opinion articles, systematic reviews, and topical survey articles from the fields of Cognitive Science (including Philosophy of Cognitive Science), Artificial Intelligence/Computer Science, Cognitive Robotics, Developmental Science, Psychology, and Neuroscience and Neuromorphic Engineering. Empirical studies will be considered if they are supplemented by theoretical analyses and contributions to theory development and/or computational modelling studies.