Kunstliche Intelligenz人工智能（德文刊名）
Kunstliche Intelligenz(英文缩写 KUNSTL INTELL),ISSN 0933-1875,eISSN 1610-1987,中文译名:人工智能（德文刊名） 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 未收录 | N/A |
| 2022 | 2.900 | N/A |
| 2023 | 2.800 | Q2 |
| 2024 | 3.600 | Q2 |
| 2025 | 3.200 | Q3 |
Kunstliche Intelligenz 最新收录文献
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1. Report on "Axiomatizing Conditional Normative Reasoning".
1. 关于“条件规范推理的公理化”的报告PMID:日期:2024-03-06This is a report on the project "Axiomatizing Conditional Normative Reasoning" (ANCoR, M 3240-N) funded by the Austrian Science Fund (FWF). The project aims to deepen our understanding of conditional normative reasoning by providing an axiomatic study of it at the propositional but also first-order level. The focus is on a particular framework, the so-called preference-based logic for conditional obligation, whose main strength has to do with the treatment of contrary-to-duty reasoning and reasoning about exceptions. The project considers not only the meta-theory of this family of logics but also its mechanization.
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2. Predicting Individual Treatment Effects: Challenges and Opportunities for Machine Learning and Artificial Intelligence.
2. 预测个体治疗效果：机器学习与人工智能的挑战与机遇PMID:日期:2024-01-22Personalized medicine seeks to identify the right treatment for the right patient at the right time. Predicting the treatment effect for an individual patient has the potential to transform treatment of patients and drastically improve patients outcomes. In this work, we illustrate the potential for ML and AI methods to yield useful predictions of individual treatment effects. Using the predicted individual treatment effects (PITE) framework which uses baseline covariates (features) to predict whether a treatment is expected to yield benefit for a given patient compared to an alternative intervention we provide an illustration of the potential of such approaches and provide a detailed discussion of opportunities for further research and open challenges when seeking to predict individual treatment effects.
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3. In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies.
3. 从街景图像数据中寻找地下室指标：数据来源与分析策略的调查研究PMID:日期:2023-01-20Street view imagery databases such as Google Street View, Mapillary, and Karta View provide great spatial and temporal coverage for many cities globally. Those data, when coupled with appropriate computer vision algorithms, can provide an effective means to analyse aspects of the urban environment at scale. As an effort to enhance current practices in urban flood risk assessment, this project investigates a potential use of street view imagery data to identify building features that indicate buildings' vulnerability to flooding (e.g., basements and semi-basements). In particular, this paper discusses (1) building features indicating the presence of basement structures, (2) available imagery data sources capturing those features, and (3) computer vision algorithms capable of automatically detecting the features of interest. The paper also reviews existing methods for reconstructing geometry representations of the extracted features from images and potential approaches to account for data quality issues. Preliminary experiments were conducted, which confirmed the usability of the freely available Mapillary images for detecting basement railings as an example type of basement features, as well as geolocating the features.
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4. Generating Explanations for Conceptual Validation of Graph Neural Networks: An Investigation of Symbolic Predicates Learned on Relevance-Ranked Sub-Graphs.
4. 为图神经网络概念验证生成解释：关于在相关性排序子图上学习符号谓词的研究PMID:日期:2022-11-07Graph Neural Networks (GNN) show good performance in relational data classification. However, their contribution to concept learning and the validation of their output from an application domain's and user's perspective have not been thoroughly studied. We argue that combining symbolic learning methods, such as Inductive Logic Programming (ILP), with statistical machine learning methods, especially GNNs, is an essential forward-looking step to perform powerful and validatable relational concept learning. In this contribution, we introduce a benchmark for the conceptual validation of GNN classification outputs. It consists of the symbolic representations of symmetric and non-symmetric figures that are taken from a well-known Kandinsky Pattern data set. We further provide a novel validation framework that can be used to generate comprehensible explanations with ILP on top of the relevance output of GNN explainers and human-expected relevance for concepts learned by GNNs. Our experiments conducted on our benchmark data set demonstrate that it is possible to extract symbolic concepts from the most relevant explanations that are representative of what a GNN has learned. Our findings open up a variety of avenues for future research on validatable explanations for GNNs.
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5. A Framework for Learning Event Sequences and Explaining Detected Anomalies in a Smart Home Environment.
PMID:日期:2022-10-31This paper presents a framework for learning event sequences for anomaly detection in a smart home environment. It addresses environment conditions, device grouping, system performance and explainability of anomalies. Our method models user behavior as sequences of events, triggered by interaction of the home residents with the Internet of Things (IoT) devices. Based on a given set of recorded event sequences, the system can learn the habitual behavior of the residents. An anomaly is described as deviation from that normal behavior, previously learned by the system. One key feature of our framework is the explainability of detected anomalies, which is implemented through a simple rule analysis.
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6. Some Thoughts on AI Stimulated by Michael Wooldridge's Book "The Road to Conscious Machines. The Story of AI".
6. 由迈克尔·伍尔德里奇所著《通往意识机器之路：人工智能的故事》一书引发的一些关于人工智能的思考PMID:日期:2022-07-28Following a brief over view of the contents of Michael Wooldridge's book I give an account of my own background in computing and AI. I then cover a range of topics stimulated by reading the book including machine learning's relationship to AI, applications to medical areas, the need to consider probabilistic effects on decisions, the importance of self-reproduction and whether AI can be made moral. I finish with a discussion of the mind-brain relationship and what makes us human.
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7. Simplifying Programming for Non-technical Students: A Hermeneutic Approach.
PMID:日期:2022-01-17This paper investigates the simplification of programming for non-technical university students. Typical simplification strategies are outlined, and according to our findings CT courses for non-technical students typically address learners from different faculties, providing generic and basic knowledge, not specifically related to their major. In this study, we propose instead a hermeneutic approach to simplify programming, in which we aim at clarifying the problem-solving aspect of programming, addressing computational problems that are specific to their studies and leveraging on learners' preunderstanding of the digital media they have experienced as users. The practical counterpart of our theoretical approach is a minimalistic Python multimedia library, called Medialib, that we designed to enable university students with a non-technical profile to create visual media and games with short and readable code. We discuss the use of Medialib in two empirical case studies: a collaboration with the university of Kyushu in Fukuoka, Japan, and a coding module for Media Studies students at the University of Southern Denmark. Furthermore, we use Notional Machines to attempt a comparison of the simplicity of learning tools for programming, and to ground our claim that Medialib is "simpler" for learners than other popular approaches. The main contribution is a hermeneutic approach to the simplification of programming for specific contexts that combines the hermeneutic spiral and notional machines. The approach is supported by a tool, the Medialib library; the two case studies provide examples of how the approach and tool can be deployed in beginners in CT courses.
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9. Contextualizing AI Education for K-12 Students to Enhance Their Learning of AI Literacy Through Culturally Responsive Approaches.
9. 将面向K-12学生的AI教育置于情境之中，通过文化回应性方法增强其AI素养的学习PMID:日期:2021-08-06AI has become ubiquitous in our society, accelerated by the speed of the development of machine learning algorithms and voice and facial recognition technologies used in our everyday lives. Furthermore, AI-enhanced technologies and tools are no strangers in the field of education. It is more evident that it is important to prepare K-12 population of students for their future professions as well as citizens capable of understanding and utilizing AI-enhanced technologies in the future. In response to such needs, the authors started a collaborative project aiming to provide a K-12 AI curriculum for Japanese students. However, the authors soon realized that it is important to contextualize the learning experience for the targeted K-12 students. The paper aims at introducing the idea of contextualizing AI education and learning experience of K-12 students with examples and tips using the work-in-progress version of the contextualized curriculum using culturally responsive approaches to promote the awareness and understanding of AI ethics among middle school students.
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10. Comparing 2 Years of Empowering Families to Solve Real-World Problems with AI.
PMID:日期:2021-07-22Over the course of 2 years a global technology education nonprofit engaged ~ 20,000 under-resourced 3rd-8th grade students, parents and educators from 13 countries in a multi-week AI competition. Families worked together with the help of educators to identify meaningful problems in their communities and developed AI-prototypes to address them. Key findings included: (1) Identifying a high level of interest in underserved communities to develop and apply AI-literacy skills; (2) Determining curricular and program implementation elements that enable families to apply AI knowledge and skills to real problems; (3) Identifying effective methods of engaging industry mentors to support participants; (4) Measuring and identifying changes in self-efficacy and ability to apply AI-based tools to real-world problems; (5) Determining effective curricula around value-sensitive design and ethical innovation.