Martin, Andreas

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Martin
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Andreas
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Martin, Andreas

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2020 - 20223
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Autor:in: Lenat, Doug × Autor:in: van Harmelen, Frank × Enddatum: 2022 × Anfangsdatum: 2020 × Institut: Institut für Wirtschaftsinformatik ×

Suchergebnisse

Gerade angezeigt 1 - 3 von 3
  • Vorschaubild
    Publikation
    A hybrid intelligent approach for the support of higher education students in literature discovery
    (2022) Prater, Ryan; Laurenzi, Emanuele; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard; van Harmelen, Frank [in: Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022)]
    In this paper, we present a hybrid intelligent approach that combines knowledge engineering, machine learning, and human intervention to automatically recommend literature resources relevant for a high quality of literature discovery. The primary target group that we aim to support is higher education students in their first experiences with research works. The approach builds a knowledge graph by leveraging a logistic regression algorithm which is first parameterized and then influenced by the interventions of a supervisor and a student, respectively. Both interventions allow continuous learning based on both the supervisor’s preferences (e.g. high score of H-index) and the student’s feedback to the resulting literature resources. The creation of the hybrid intelligent approach followed the Design-Science Research methodology and is instantiated in a working prototype named PaperZen. The evaluation was conducted in two complementary ways: (1) by showing how the design requirements manifest in the prototype, and (2) with an illustrative scenario in which a corpus of a research project was taken as a source of truth. A small subset from the corpus was entered into the PaperZen and Google Scholar, independently. The resulting literature resources were compared with the corpus of a research project and show that PaperZen outperforms Google Scholar
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Towards AI-based solutions in the system development lifecycle
    (2020) Jüngling, Stephan; Peraic, Martin; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard; van Harmelen, Frank [in: Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020)]
    Many teams across different industries and organizations explicitly apply agile methodologies such as Scrum in their system development lifecycle (SDLC). The choice of the technology stack, the programming language, or the decision whether AI solutions could be incorporated into the system design either is given by corporate guidelines or is chosen by the project team based on their individual skill set. The paper describes the business case of implementing an AI-based automatic passenger counting system for public transportation, shows preliminary results of the prototype using anonymous passenger recognition on the edge with the help of Google Coral devices.It shows how different solutions could be integrated with the help of rule base systems and how AI-based solutions could be established in the SDLC as valid and cost-saving alternatives to traditionally programmed software components.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Combining symbolic and sub-symbolic AI in the context of education and learning
    (2020) Telesko, Rainer; Jüngling, Stephan; Gachnang, Phillip; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard; van Harmelen, Frank [in: Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020)]
    Abstraction abilities are key to successfully mastering the Business Information Technology Programme (BIT) at the FHNW (Fachhochschule Nordwestschweiz). Object-Orientation (OO) is one example - which extensively requires analytical capabilities. For testing the OO-related capabilities a questionnaire (OO SET) for prospective and 1st year students was developed based on the Blackjack scenario. Our main target of the OO SET is to identify clusters of students which are likely to fail in the OO-related modules without a substantial amount of training. For the interpretation of the data the Kohonen Feature Map (KFM) is used which is nowadays very popular for data mining and exploratory data analysis. However, like all sub-symbolic approaches the KFM lacks to interpret and explain its results. Therefore, we plan to add - based on existing algorithms - a “postprocessing” component which generates propositional rules for the clusters and helps to improve quality management in the admission and teaching process. With such an approach we synergistically integrate symbolic and sub-symbolic artificial intelligence by building a bridge between machine learning and knowledge engineering.
    04B - Beitrag Konferenzschrift