Christian Fleiner’s research while affiliated with KU Leuven and other places

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Publications (7)


Towards Effective Management of Verbal Probability Expressions Using a Co-Learning Approach
  • Chapter

June 2024

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4 Reads

Christian Fleiner

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Domain experts are one of the most important knowledge sources when building a knowledge base. However, communication about uncertain states and events is prone to misinterpretations and misunderstandings, because people prefer to convey probability estimations by verbal probability expressions (VPEs) which have a high between-subject variability. Additionally, several biases exist when expressing uncertainty verbally. Nevertheless, the application of VPEs might be necessary. Therefore, means must be identified to manage VPEs and to translate them into numeric values appropriately. In this paper, we propose a co-learning approach with example to efficiently and effectively communicate (subjective) probabilities of states and events in teams where human and AI team members are familiarized with the translation between VPEs and numeric values until both parties are capable of using solely numeric values.




Figure 2. Overview of the hybrid human-AI teams and their roles in the context of a first response use case.
Figure 3. A hybrid human-AI maintenance team and the roles of the agents.
Figure 4. A hybrid human-AI team and their roles in the context of monitoring animal wildlife.
Figure 5. A hybrid human-AI team aiming at providing personalized (emotional) care to young patients.
Developing Team Design Patterns for Hybrid Intelligence Systems
  • Chapter
  • Full-text available

June 2023

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373 Reads

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2 Citations

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Mani Tajaddini

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[...]

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With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured way of specifying design solutions of collaboration for hybrid intelligence (HI) systems and there is a lack of best practices shared across application domains. We address this gap by investigating the generalization of specific design solutions into design patterns that can be shared and applied in different contexts. We present a human-centered bottom-up approach for the specification of design solutions and their abstraction into team design patterns. We apply the proposed approach for 4 concrete HI use cases and show the successful extraction of team design patterns that are generalizable, providing re-usable design components across various domains. This work advances previous research on team design patterns and designing applications of HI systems.

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Citations (2)


... In the context of our photo book use case, we aim at using, e.g., active learning and principles from human-in-the-loop expert systems. The greater goal is to perform application tasks more satisfactorily: human-machine teams shall surpass the efficiency/effectiveness of humans or machines in this task alone [van Zoelen et al. 2023]. ...

Reference:

A look under the hood of the Interactive Deep Learning Enterprise (No-IDLE)
Developing Team Design Patterns for Hybrid Intelligence Systems

... More importantly, the system was perceived as highly efficient for users with cognitive impairments because the cognitive workload is lower if such a system is used. Fleiner et al. (2021) argued that conversational user interfaces are not sufficiently robust for maintenance guidance because of the ambient noise that interferes with voice recognition. Therefore, they proposed a set of user-defined gestural inputs (hand and head) as a complement to text-and voice-based communication. ...

Ensuring a Robust Multimodal Conversational User Interface During Maintenance Work
  • Citing Conference Paper
  • September 2021