07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/8

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    ItemOpen Access
    Are you sure? : prediction revision in automated decision‐making
    (2020) Burkart, Nadia; Robert, Sebastian; Huber, Marco F.
    With the rapid improvements in machine learning and deep learning, decisions made by automated decision support systems (DSS) will increase. Besides the accuracy of predictions, their explainability becomes more important. The algorithms can construct complex mathematical prediction models. This causes insecurity to the predictions. The insecurity rises the need for equipping the algorithms with explanations. To examine how users trust automated DSS, an experiment was conducted. Our research aim is to examine how participants supported by an DSS revise their initial prediction by four varying approaches (treatments) in a between‐subject design study. The four treatments differ in the degree of explainability to understand the predictions of the system. First we used an interpretable regression model, second a Random Forest (considered to be a black box [BB]), third the BB with a local explanation and last the BB with a global explanation. We noticed that all participants improved their predictions after receiving an advice whether it was a complete BB or an BB with an explanation. The major finding was that interpretable models were not incorporated more in the decision process than BB models or BB models with explanations.
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    Erfassung und Berücksichtigung von Persönlichkeitsmerkmalen im Kontext der ergonomischen Fahrzeugauslegung
    (Stuttgart : Institut für Konstruktionstechnik und Technisches Design, 2022) Pomiersky, Philipp Sebastian; Maier, Thomas (Univ.-Prof. Dr.-Ing.)
    Die vorliegende Arbeit behandelt die Optimierung der ergonomischen Fahrzeugauslegung durch die Miteinbeziehung von Persönlichkeitsmerkmalen. Trotz der Berücksichtigung vieler Einflussfaktoren, wie Anthropometrie oder Sinnesphysiologie, verbleibt bei vielen Aspekten der Makroergonomie im Sinne des Technischen Designs eine bisher nicht erklärbare Streuung zwischen Personen. Um die Passung zwischen Nutzenden und Fahrzeug zu optimieren, müssen zukünftig Persönlichkeitsmerkmale berücksichtigt werden. Allerdings ist bisher weder bekannt, welche Persönlichkeitsmerkmale ergonomierelevant sind, noch gibt es ein spezifisches Messinstrument hierfür. Aus dieser Problemstellung ergibt sich das Ziel dieser Arbeit, ergonomierelevante Persönlichkeitsmerkmale zu detektieren, messbar zu machen und erste Einflüsse dieser Persönlichkeitsmerkmale auf die Makroergonomie zu ermitteln. Mit einem mehrstufigen systematischen expertenbasierten Verfahren wird ermittelt, dass die Persönlichkeitsmerkmale Fahrspaß, Sicherheitsbedürfnis, Komfortaffinität, Diskomfortempfindlichkeit, Infotainmentorientierung, Gewohnheit, Ergonomiebewusstsein und Aufwandsvermeidung den größten Einfluss auf die Makroergonomie haben. Zur Messung der Merkmale wird der Fragebogen „Ergonomierelevantes Persönlichkeitsinventar für das Auto“ (ERPI-A) systematisch mithilfe diverser Methoden mit Fachleuten und Autofahrenden entwickelt. Die Überprüfung der Gütekriterien zeigt, dass ERPI-A die Persönlichkeitsmerkmale valide und ökonomisch misst. Um ERPI-A im praktischen Einsatz zu testen und erste Zusammenhänge mit der Makroergonomie zu detektieren, wird eine breit aufgestellte Anwendungsstudie im Fahrzeug-Ergonomie-Prüfstand konzipiert und durchgeführt. Der Fokus liegt dabei auf der Körperhaltung, Sitz- und Lenkrad-position, dem Einstellvorgang von Sitz und Lenkrad, der Berücksichtigung des Infotainmentsystems während der Fahrt sowie der Bewertung von Fahrzeug-, Maß- und Bedienkonzept. Dabei haben alle Persönlichkeitsmerkmale Auswirkungen auf Teilaspekte der Makroergonomie. Aufbauend auf den Versuchsergebnissen werden Empfehlungen für den zukünftigen Einsatz von ERPI-A hinsichtlich der Durchführung und Interpretation von Untersuchungen und der Anpassung der Fahrzeugkonzeption gegeben.
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    Combining brain-computer interfaces with deep reinforcement learning for robot training : a feasibility study in a simulation environment
    (2023) Vukelić, Mathias; Bui, Michael; Vorreuther, Anna; Lingelbach, Katharina
    Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously learn complex tasks. While sparsity is a natural way to define a reward in realistic robot scenarios, it provides poor learning signals for the agent, thus making the design of good reward functions challenging. To overcome this challenge learning from human feedback through an implicit brain-computer interface (BCI) is used. We combined a BCI with deep RL for robot training in a 3-D physical realistic simulation environment. In a first study, we compared the feasibility of different electroencephalography (EEG) systems (wet- vs. dry-based electrodes) and its application for automatic classification of perceived errors during a robot task with different machine learning models. In a second study, we compared the performance of the BCI-based deep RL training to feedback explicitly given by participants. Our findings from the first study indicate the use of a high-quality dry-based EEG-system can provide a robust and fast method for automatically assessing robot behavior using a sophisticated convolutional neural network machine learning model. The results of our second study prove that the implicit BCI-based deep RL version in combination with the dry EEG-system can significantly accelerate the learning process in a realistic 3-D robot simulation environment. Performance of the BCI-based trained deep RL model was even comparable to that achieved by the approach with explicit human feedback. Our findings emphasize the usage of BCI-based deep RL methods as a valid alternative in those human-robot applications where no access to cognitive demanding explicit human feedback is available.
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    Oscillatory EEG signatures of affective processes during interaction with adaptive computer systems
    (2020) Vukelić, Mathias; Lingelbach, Katharina; Pollmann, Kathrin; Peissner, Matthias
    Affect monitoring is being discussed as a novel strategy to make adaptive systems more user-oriented. Basic knowledge about oscillatory processes and functional connectivity underlying affect during naturalistic human–computer interactions (HCI) is, however, scarce. This study assessed local oscillatory power entrainment and distributed functional connectivity in a close-to-naturalistic HCI-paradigm. Sixteen participants interacted with a simulated assistance system which deliberately evoked positive (supporting goal-achievement) and negative (impeding goal-achievement) affective reactions. Electroencephalography (EEG) was used to examine the reactivity of the cortical system during the interaction by studying both event-related (de-)synchronization (ERD/ERS) and event-related functional coupling of cortical networks towards system-initiated assistance. Significantly higher α-band and β-band ERD in centro-parietal and parieto-occipital regions and β-band ERD in bi-lateral fronto-central regions were observed during impeding system behavior. Supportive system behavior activated significantly higher γ-band ERS in bi-hemispheric parietal-occipital regions. This was accompanied by functional coupling of remote β-band and γ-band activity in the medial frontal, left fronto-central and parietal regions, respectively. Our findings identify oscillatory signatures of positive and negative affective processes as reactions to system-initiated assistance. The findings contribute to the development of EEG-based neuroadaptive assistance loops by suggesting a non-obtrusive method for monitoring affect in HCI.