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Cognitive Reasoning and Trust in Human-Robot Interactions

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Theory and Applications of Models of Computation (TAMC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10185))

Abstract

We are witnessing accelerating technological advances in autonomous systems, of which driverless cars and home-assistive robots are prominent examples. As mobile autonomy becomes embedded in our society, we increasingly often depend on decisions made by mobile autonomous robots and interact with them socially. Key questions that need to be asked are how to ensure safety and trust in such interactions. How do we know when to trust a robot? How much should we trust? And how much should the robots trust us? This paper will give an overview of a probabilistic logic for expressing trust between human or robotic agents such as “agent A has 99% trust in agent B’s ability or willingness to perform a task” and the role it can play in explaining trust-based decisions and agent’s dependence on one another. The logic is founded on a probabilistic notion of belief, supports cognitive reasoning about goals and intentions, and admits quantitative verification via model checking, which can be used to evaluate trust in human-robot interactions. The paper concludes by summarising future challenges for modelling and verification in this important field.

This work is supported by EPSRC Mobile Autonomy Programme Grant EP/M019918/1.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Oxford, Oxford, UK

    Marta Kwiatkowska

Authors
  1. Marta Kwiatkowska
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Correspondence to Marta Kwiatkowska .

Editor information

Editors and Affiliations

  1. Anna University, Chennai, India

    T.V. Gopal

  2. Universität Bern , Bern, Switzerland

    Gerhard Jäger

  3. Universität Bern , Bern, Switzerland

    Silvia Steila

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Kwiatkowska, M. (2017). Cognitive Reasoning and Trust in Human-Robot Interactions. In: Gopal, T., Jäger , G., Steila, S. (eds) Theory and Applications of Models of Computation. TAMC 2017. Lecture Notes in Computer Science(), vol 10185. Springer, Cham. https://doi.org/10.1007/978-3-319-55911-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-55911-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55910-0

  • Online ISBN: 978-3-319-55911-7

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