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Teaming Up with Artificial Agents in Non-routine Analytical Tasks

Author

Listed:
  • Lorenzo Cominelli
  • Federico Galatolo
  • Caterina Giannetti
  • Cristiano Ciaccio
  • Felice Dell’Orletta
  • Philipp Chaposkvi
  • Giulia Venturi

Abstract

Using a real-life escape room scenario, we investigate how different levels of embodiment in artificial agents influence team performance and conversational dynamics in non-routine analytical tasks. Teams composed of either three humans or two humans and an artificial agent (a Box, an Avatar, and a hyper-realistic Humanoid) worked together to escape the room within a time limit. Our findings reveal that while human-only teams tend to complete all tasks more frequently, they also tend to be slower and make more errors. Additionally, we observe a non-linear relationship between the degree of agent embodiment and team performance, with a significant effect on conversational dynamics. Teams with agents exhibiting higher levels of embodiment display conversational patterns more similar to those occurring among humans. These results highlight the complex role that embodied AI plays in human-agent interactions, offering new insights into how artificial agents can be designed to support team collaboration in problem-solving environments.

Suggested Citation

  • Lorenzo Cominelli & Federico Galatolo & Caterina Giannetti & Cristiano Ciaccio & Felice Dell’Orletta & Philipp Chaposkvi & Giulia Venturi, 2024. "Teaming Up with Artificial Agents in Non-routine Analytical Tasks," Discussion Papers 2024/314, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    • Handle: RePEc:pie:dsedps:2024/314
    Note: ISSN 2039-1854
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    References listed on IDEAS

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    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior

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