Jüngling, Stephan
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Jüngling, Stephan
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- PublikationDecision support combining machine learning, knowledge representation and case-based reasoning(Sun SITE, Informatik V, RWTH Aachen, 2021) Mehli, Carlo; Hinkelmann, Knut; Jüngling, Stephan [in: Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021)]Knowledge and knowledge work are essential for the success of companies nowadays. Decisions are based on knowledge and better knowledge leads to more informed decisions. Therefore, the management of knowledge and support of decision making has increasingly become a source of competitive advantage for organizations. The current research uses a design science research approach (DSR) with the aim to improve the decision making of a knowledge intensive process such as the student admission process, which is done manually until now. In the awareness phase of the DSR process, the case study research method is applied to analyze the decision making and the knowledge that is needed to derive the decisions. Based on the analysis of the application scenario, suitable methods to support decision making were identified. The resulting system design is based on a combination of Case-Based Reasoning (CBR) and Machine Learning (ML). The proposed system design and prototype has been validated using triangulation evaluation, to assess the impact of the proposed system on the application scenario. The evaluation revealed that the additional hints from CBR and ML can assist the deans of the study program to improve the knowledge management and increase the quality, transparency and consistency of the decision-making process in the student application process. Furthermore, the proposed approach fosters the exchange of knowledge among the different process participants involved and codifies previously tacit knowledge to some extent and provides relevant externalized knowledge to decision makers at the required moment. The designed prototype showcases how ML and CBR methodologies can be combined to support decision making in knowledge intensive processes and finally concludes with potential recommendations for future research.04B - Beitrag Konferenzschrift
- PublikationCombining 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
- PublikationTowards 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
- PublikationTowards an assistive and pattern learning-driven process modeling approach(2019) Laurenzi, Emanuele; Hinkelmann, Knut; Jüngling, Stephan; Montecchiari, Devid; Pande, Charuta; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; van Harmelen, Frank; Clark, Peter [in: Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)]The practice of business process modeling not only requires modeling expertise but also significant domain expertise. Bringing the latter into an early stage of modeling contributes to design models that appropriately capture an underlying reality. For this, modeling experts and domain experts need to intensively cooperate, especially when the former are not experienced within the domain they are modeling. This results in a time-consuming and demanding engineering effort. To address this challenge, we propose a process modeling approach that assists domain experts in the creation and adaptation of process models. To get an appropriate assistance, the approach is driven by semantic patterns and learning. Semantic patterns are domain-specific and consist of process model fragments (or end-to-end process models), which are continuously learned from feedback from domain as well as process modeling experts. This enables to incorporate good practices of process modeling into the semantic patterns. To this end, both machine-learning and knowledge engineering techniques are employed, which allow the semantic patterns to adapt over time and thus to keep up with the evolution of process modeling in the different business domains.04B - Beitrag Konferenzschrift
- PublikationLeverage white-collar workers with AI(2019) Jüngling, Stephan; Hofer, Angelin; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; Clark, Peter [in: Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)]Based on the example of automated meeting minutes taking, the paper highlights the potential of optimizing the allocation of tasks between humans and machines to take the particular strengths and weaknesses of both into account. In order to combine the functionality of supervised and unsupervised machine learning with rule-based AI or traditionally programmed software components, the capabilities of AI-based system actors need to be incorporated into the system design process as early as possible.04B - Beitrag Konferenzschrift
- PublikationTowards an Agile Requirements Engineering Process combining HERMES 5 and SCRUM(2015) Schär, Birgit; Jüngling, Stephan; Thönssen, Barbara; Hinkelmann, Knut; Thönssen, Barbara [in: ES2015, Third International Conference on Enterprise Systems, Basel, 14-15 October 2015]04B - Beitrag Konferenzschrift