Auflistung nach Autor:in "Kersting, Kristian"
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- ZeitschriftenartikelCan Computers Learn from the Aesthetic Wisdom of the Crowd?(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Bauckhage, Christian; Kersting, KristianThe social media revolution has led to an abundance of image and video data on the Internet. Since this data is typically annotated, rated, or commented upon by large communities, it provides new opportunities and challenges for computer vision. Social networking and content sharing sites seem to hold the key to the integration of context and semantics into image analysis. In this paper, we explore the use of social media in this regard. We present empirical results obtained on a set of 127,593 images with 3,741,176 tag assignments that were harvested from Flickr, a photo sharing site. We report on how users tag and rate photos and present an approach towards automatically recognizing the aesthetic appeal of images using confidence-based classifiers to alleviate effects due to ambiguously labeled data. Our results indicate that user generated content allows for learning about aesthetic appeal. In particular, established low-level image features seem to enable the recognition of beauty. A reliable recognition of unseemliness, on the other hand, appears to require more elaborate high-level analysis.
- ZeitschriftenartikelData Mining and Pattern Recognition in Agriculture(KI - Künstliche Intelligenz: Vol. 27, No. 4, 2013) Bauckhage, Christian; Kersting, KristianModern communication, sensing, and actuator technologies as well as methods from signal processing, pattern recognition, and data mining are increasingly applied in agriculture. Developments such as increased mobility, wireless networks, new environmental sensors, robots, and the computational cloud put the vision of a sustainable agriculture for anybody, anytime, and anywhere within reach. Yet, precision farming is a fundamentally new domain for computational intelligence and constitutes a truly interdisciplinary venture. Accordingly, researchers and experts of complementary skills have to cooperate in order to develop models and tools for data intensive discovery that allow for operation through users that are not necessarily trained computer scientists. We present approaches and applications that address these challenges and underline the potential of data mining and pattern recognition in agriculture.
- ZeitschriftenartikelExplanatory Interactive Machine Learning(Business & Information Systems Engineering: Vol. 65, No. 6, 2023) Pfeuffer, Nicolas; Baum, Lorenz; Stammer, Wolfgang; Abdel-Karim, Benjamin M.; Schramowski, Patrick; Bucher, Andreas M.; Hügel, Christian; Rohde, Gernot; Kersting, Kristian; Hinz, OliverThe most promising standard machine learning methods can deliver highly accurate classification results, often outperforming standard white-box methods. However, it is hardly possible for humans to fully understand the rationale behind the black-box results, and thus, these powerful methods hamper the creation of new knowledge on the part of humans and the broader acceptance of this technology. Explainable Artificial Intelligence attempts to overcome this problem by making the results more interpretable, while Interactive Machine Learning integrates humans into the process of insight discovery. The paper builds on recent successes in combining these two cutting-edge technologies and proposes how Explanatory Interactive Machine Learning (XIL) is embedded in a generalizable Action Design Research (ADR) process – called XIL-ADR. This approach can be used to analyze data, inspect models, and iteratively improve them. The paper shows the application of this process using the diagnosis of viral pneumonia, e.g., Covid-19, as an illustrative example. By these means, the paper also illustrates how XIL-ADR can help identify shortcomings of standard machine learning projects, gain new insights on the part of the human user, and thereby can help to unlock the full potential of AI-based systems for organizations and research.
- JournalFrom Big Data to Big Artificial Intelligence?(KI - Künstliche Intelligenz: Vol. 32, No. 1, 2018) Kersting, Kristian; Meyer, Ulrich
- ZeitschriftenartikelKünstliche Intelligenz für Computerspiele(Informatik-Spektrum: Vol. 37, No. 6, 2014) Bauckhage, Christian; Kersting, Kristian; Thurau, ChristianDie technische Entwicklung von Computerspielen und die Entwicklung von Methoden der Künstlichen Intelligenz (KI) gehen seit Jahrzehnten Hand in Hand. Spektakuläre Erfolge der KI in Spieleszenarien sind etwa der Sieg des Schachcomputers Deep Blue über den damaligen Weltmeister Gary Kasparow im Jahr 1997 oder der Gewinn der Quizshow Jeopardy durch das Programm Watson im Jahr 2010. Standen lange Zeit Fragen zur Implementierung möglichst intelligenter und glaubwürdiger künstlicher Spieler im Vordergrund, ergeben sich durch aktuelle Entwicklungen in den Bereichen mobile- und social gaming neue Problemstellungen für die KI. Dieser Artikel beleuchtet die historische Entwicklung der KI in Computerspielen und diskutiert die Herausforderungen, die sich in modernen Spieleszenarien ergeben.
- TextdokumentKünstliche Intelligenz – Die dritte Welle(INFORMATIK 2020, 2021) Schmid, Ute; Tresp, Volker; Bethge, Matthias; Kersting, Kristian; Stiefelhagen, RainerAktuelle Forschungsarbeiten aus dem Bereich Künstlichen Intelligenz werden vorgestellt. Dabei werden drei Perspektiven auf das Gebiet Maschinelles Lernen präsentiert, die über rein datenintensive Blackbox-Verfahren hinausgehen: Es werden Methoden vorgestellt, mit denen Erklärungen für die Entscheidungen von KI-Systemen generiert werden, aktuelle neurowissenschaftlich Ansätze zum maschinellen Sehen gezeigt und eine Möglichkeit Vorwissen in den Prozess des machinellen Lernens einzubringen aufgezeigt.
- JournalMaking AI Smarter(KI - Künstliche Intelligenz: Vol. 32, No. 4, 2018) Kersting, Kristian
- ZeitschriftenartikelMatrix- and Tensor Factorization for Game Content Recommendation(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Sifa, Rafet; Yawar, Raheel; Ramamurthy, Rajkumar; Bauckhage, Christian; Kersting, KristianCommercial success of modern freemium games hinges on player satisfaction and retention. This calls for the customization of game content or game mechanics in order to keep players engaged. However, whereas game content is already frequently generated using procedural content generation, methods that can reliably assess what kind of content suits a player’s skills or preferences are still few and far between. Addressing this challenge, we propose novel recommender systems based on latent factor models that allow for recommending quests in a single player role-playing game. In particular, we introduce a tensor factorization algorithm to decompose collections of bipartite matrices which represent how players’ interests and behaviors change over time. Extensive online bucket type tests during the ongoing operation of a commercial game reveal that our system is able to recommend more engaging quests and to retain more players than previous handcrafted or collaborative filtering approaches.
- ZeitschriftenartikelRethinking Computer Science Through AI(KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Kersting, Kristian
- ZeitschriftenartikelSemantic Interpretation of Multi-Modal Human-Behaviour Data(KI - Künstliche Intelligenz: Vol. 31, No. 4, 2017) Bhatt, Mehul; Kersting, KristianThis special issue presents interdisciplinary research—at the interface of artificial intelligence, cognitive science, and human-computer interaction—focussing on the semantic interpretation of human behaviour. The special issue constitutes an attempt to highlight and steer foundational methods research in artificial intelligence, in particular knowledge representation and reasoning, for the development of human-centred cognitive assistive technologies. Of specific interest and focus have been application outlets for basic research in knowledge representation and reasoning and computer vision for the cognitive, behavioural, and social sciences.