Salikutluk, Vildan ; Schöpper, Janik ; Herbert, Franziska ; Scheuermann, Katrin ; Frodl, Eric ; Balfanz, Dirk ; Jäkel, Frank ; Koert, Dorothea (2024)
An Evaluation of Situational Autonomy for Human-AI Collaboration in a Shared Workspace Setting.
2024 CHI Conference on Human Factors in Computing Systems. Honolulu, USA (11.05.-16.05.2024)
doi: 10.1145/3613904.3642564
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Designing interactions for human-AI teams (HATs) can be challenging due to an AI agent’s potential autonomy. Previous work suggests that higher autonomy does not always improve team performance, and situation-dependent autonomy adaptation might be beneficial. However, there is a lack of systematic empirical evaluations of such autonomy adaptation in human-AI interaction. Therefore, we propose a cooperative task in a simulated shared workspace to investigate effects of fixed levels of AI autonomy and situation-dependent autonomy adaptation on team performance and user satisfaction. We derive adaptation rules for AI autonomy from previous work and a pilot study. We implement these rule for our main experiment and find that team performance was best when humans collaborated with an agent adjusting its autonomy based on the situation. Additionally, users rated this agent highest in terms of perceived intelligence. From these results, we discuss the influence of varying autonomy degrees on HATs in shared workspaces.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Salikutluk, Vildan ; Schöpper, Janik ; Herbert, Franziska ; Scheuermann, Katrin ; Frodl, Eric ; Balfanz, Dirk ; Jäkel, Frank ; Koert, Dorothea |
| Art des Eintrags: | Bibliographie |
| Titel: | An Evaluation of Situational Autonomy for Human-AI Collaboration in a Shared Workspace Setting |
| Sprache: | Englisch |
| Publikationsjahr: | 11 Mai 2024 |
| Verlag: | ACM |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | An Evaluation of Situational Autonomy for Human-AI Collaboration in a Shared Workspace Setting |
| Buchtitel: | CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems |
| Kollation: | 17 Seiten |
| Veranstaltungstitel: | 2024 CHI Conference on Human Factors in Computing Systems |
| Veranstaltungsort: | Honolulu, USA |
| Veranstaltungsdatum: | 11.05.-16.05.2024 |
| DOI: | 10.1145/3613904.3642564 |
| Kurzbeschreibung (Abstract): | Designing interactions for human-AI teams (HATs) can be challenging due to an AI agent’s potential autonomy. Previous work suggests that higher autonomy does not always improve team performance, and situation-dependent autonomy adaptation might be beneficial. However, there is a lack of systematic empirical evaluations of such autonomy adaptation in human-AI interaction. Therefore, we propose a cooperative task in a simulated shared workspace to investigate effects of fixed levels of AI autonomy and situation-dependent autonomy adaptation on team performance and user satisfaction. We derive adaptation rules for AI autonomy from previous work and a pilot study. We implement these rule for our main experiment and find that team performance was best when humans collaborated with an agent adjusting its autonomy based on the situation. Additionally, users rated this agent highest in terms of perceived intelligence. From these results, we discuss the influence of varying autonomy degrees on HATs in shared workspaces. |
| Zusätzliche Informationen: | Art.No.: 300 |
| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie 03 Fachbereich Humanwissenschaften > Institut für Psychologie > Modelle höherer Kognition Zentrale Einrichtungen Zentrale Einrichtungen > Centre for Cognitive Science (CCS) |
| Hinterlegungsdatum: | 14 Mai 2024 11:52 |
| Letzte Änderung: | 09 Okt 2024 07:23 |
| PPN: | 522036880 |
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| Suche nach Titel in: | TUfind oder in Google |
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