Developing Team Design Patterns for Hybrid Intelligence Systems

Conference Paper (2023)
Author(s)

Emma Van Zoelen (TU Delft - Electrical Engineering, Mathematics and Computer Science, TU Delft - BUS/TNO STAFF)

Tina Mioch (TU Delft - Electrical Engineering, Mathematics and Computer Science, Hogeschool Utrecht)

Mani Tajaddini (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Christian Fleiner (Friedrich-Alexander-Universität Erlangen-Nürnberg)

Stefani Tsaneva (Technische Universität Wien, WU Wien)

Pietro Camin (University of Twente)

Thiago S. Gouvêa (DFKI GmbH)

Kim Baraka (Vrije Universiteit Amsterdam)

Maaike H.T. De Boer (DIANA FEA )

Mark A. Neerincx (TU Delft - Electrical Engineering, Mathematics and Computer Science, TU Delft - BUS/TNO STAFF)

Research Group
Interactive Intelligence
DOI related publication
https://doi.org/10.3233/FAIA230071 Final published version
More Info
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Publication Year
2023
Language
English
Research Group
Interactive Intelligence
Pages (from-to)
3-16
Publisher
IOS Press
ISBN (electronic)
9781643683942
Event
2nd International Conference on Hybrid Human Artificial Intelligence (2023-06-26 - 2023-06-30), Munich, Germany
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Abstract

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.