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Esteban Guerrero1, Maitreyee Tewari2, Panu Kalmi3

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Computer Methods and Programs in Biomedicine
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Summary

This study introduces a computational framework for social robots and humans to form shared intentions (We-intentions) and repair breakdowns. The approach aims to improve human-robot collaboration in healthcare by aligning agent goals.

Keywords:
Answer set programmingBreakdown situationsConflict of intentionsHealthcare scenariosHuman-robot interactionLogic programmingRepairing conflictsShared intentionsSocial robotsWe-intentions

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Area of Science:

  • Human-Robot Interaction
  • Artificial Intelligence
  • Social Robotics
  • Cognitive Science

Background:

  • Joint activities between humans and social robots require shared group intentions, termed We-intentions.
  • Conflicts between individual and shared intentions frequently cause breakdowns, especially in healthcare settings.
  • Addressing these breakdowns is crucial for effective human-robot collaboration.

Purpose of the Study:

  • To introduce a computational mechanism for We-intention formation and conflict reparation in human-robot interactions.
  • To demonstrate the application of this framework in healthcare scenarios.
  • To enhance the alignment of intentions between humans and social robots during joint tasks.

Main Methods:

  • A formal computational framework using Answer Set Programming and a Belief-Desire-Intention control loop was developed.
  • The framework was exemplified through theory-based user studies involving human-robot dialogue scenarios.
  • Qualitative data was gathered via semi-structured interviews with 20 participants and analyzed thematically.

Main Results:

  • Four theoretical propositions demonstrating desirable characteristics for rational social robots were proven.
  • Participants predominantly attributed breakdowns to human actions.
  • A significant portion of younger participants identified avoidant behavior in the social robot.

Conclusions:

  • The proposed framework offers a novel formalization of We-intention theory for agent alignment.
  • The computational mechanism ensures consistent plan generation and handles incomplete or inconsistent intentions.
  • Qualitative findings suggest the approach can achieve acceptable intention agreement and reparation from a human-centric viewpoint.