=Paper=
{{Paper
|id=Vol-3825/extendedAbstract5-3
|storemode=property
|title=Computational Trust in Robotics: Preliminary
Investigations and Evidence
|pdfUrl=https://ceur-ws.org/Vol-3825/extendedAbstract5-3.pdf
|volume=Vol-3825
|authors=Francesco Semeraro,Marta Romeo,Angelo Cangelosi,Samuele Vinanzi
|dblpUrl=https://dblp.org/rec/conf/hhai/SemeraroRCV24
}}
==Computational Trust in Robotics: Preliminary
Investigations and Evidence ==
https://ceur-ws.org/Vol-3825/extendedAbstract5-3.pdf
Computational Trust in Robotics: Preliminary
Investigations and Evidence
Francesco Semeraro1,∗,† , Marta Romeo2,† , Angelo Cangelosi1 and Samuele Vinanzi3,†
1
Manchester Centre for Robotics and AI, The University of Manchester, Manchester, UK
2
School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK
3
Department of Computing, Sheffield Hallam University, Sheffield, UK
Abstract
Trust plays a crucial role in the design of human-robot interaction. Most of the current research focuses on the
human factors that affect trust towards the robot, while only few works mathematically model the trust a robot
can have towards the user and viceversa. In this work, we term this line of research as “Computational Trust”
and provide empirical evidence of this trend through preliminary results from an ongoing systematic review.
Keywords
Computational Trust, Artificial Trust, Natural Trust, Human-Robot Interaction, Human-Robot Collaboration,
Social Robotics, Artificial Intelligence
1. Introduction and background
Trust is essential in shaping human-human relationships [1]. As autonomous agents are starting to enter
our everyday environments, we see an increasing number of researchers in Human-Robot Interaction
(HRI) directing their efforts towards understanding how this factor influences the relationships between
humans and intelligent machines [2]. For example, in a collaborative setting between humans and
robots, establishing a trust relationship enables the user to delegate a portion of the shared task to the
robot [3, 4, 5]. As a result, the user can concentrate on their own responsibilities within the task, thus
enhancing the overall outcome [6]. Trust in HRI has been defined as the “attitude that an agent will
help achieve an individual’s goals in a situation characterised by uncertainty and vulnerability” [7],
and it is mainly studied from the point of view of the human. In fact, most efforts have been directed
towards understanding what robotic characteristics facilitate or hinder human partners’ trust [8, 9], or
what strategies lead to trust repair once the latter is lost [10]. Research on trust and trustworthiness
in automation has become even more critical with the integration of Artificial Intelligence (AI) in the
decision-making processes of autonomous agents.
Within this research panorama, we are witnessing an increasing interest in trying to mathematically
model human trust towards robots in an attempt to understand and exploit the human partner’s internal
state [2]. We term this modeling attempt as “Natural Trust”. The focus of the latter is understanding the
human partner’s internal model of the robot to facilitate the establishment of trust. In a dual fashion,
few mathematical models address the trust that an artificial agent could have towards the user it is
interacting with, named “Artificial Trust” [11, 12]. Both these forms of trust are rarely utilized during a
human-robot interaction to alter the behavioral policy of the artificial agent.
MultiTTrust: 3rd Workshop on Multidisciplinary Perspectives on Human-AI Team Trust, June 11, 2024, Malmö, Sweden
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open francesco.semeraro@manchester.ac.uk (F. Semeraro); m.romeo@hw.ac.uk (M. Romeo);
angelo.cangelosi@manchester.ac.uk (A. Cangelosi); s.vinanzi@shu.ac.uk (S. Vinanzi)
GLOBE https://research.manchester.ac.uk/en/persons/francesco.semeraro (F. Semeraro);
https://www.edinburgh-robotics.org/academics/marta-romeo (M. Romeo);
https://research.manchester.ac.uk/en/persons/angelo.cangelosi (A. Cangelosi);
https://www.shu.ac.uk/about-us/our-people/staff-profiles/samuele-vinanzi (S. Vinanzi)
Orcid 0000-0002-8812-0968 (F. Semeraro); 0000-0003-4438-0255 (M. Romeo); 0000-0002-4709-2243 (A. Cangelosi);
0000-0003-0241-9983 (S. Vinanzi)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
From these considerations, a gap in the current state-of-the-art emerges: robots are often considered
as passive receptacles of trust, and not as active social agents that makes use of such trust to improve
their own behaviour. Starting from the consideration that trust is a bidirectional relationship needed
to successfully complete collaborative tasks [13], we are interested in investigating when and how it
is possible for robots to model the trustworthiness of their human partners [14] and/or the trust put
by the users in them, to allow them to exploit this knowledge and enhance their interactions with the
users. Attempts at regularizing the design of models of trust for robots towards their users or other
agents are still scarce, yet of great importance. Research in the current literature [15, 14] has shown
that robots that possess cognitive mechanisms to identify and anticipate mistakes or deceptions in their
human partner’s strategy (in other words, evaluating their trustworthiness) can increase the success
rate of joint collaborative tasks.
In an attempt to address this knowledge gap, we propose the term “Computational Trust” (CT) to
refer to the mathematical models that can be used by a robot or an artificial agent to perform trust
evaluations on other agents. This term incorporates both cases of Artificial Trust and Natural Trust.
We discuss initial results from a systematic review we are currently finalizing. This work aims to be the
first systematic dive into this new research domain.
2. Discussion and preliminary results
Figure 1: Keyword network of the selected set of papers (generated through VOSviewer [16]).
From the analysis of the state-of-the-art in trust in HRI detailed in Section 1, we have identified a
gap in the current research landscape. Specifically, many works presented in the literature focus on
the human side of robotic trust, i.e., the trust that humans place in robots. As we have seen, this is
an important issue to consider during the worldwide effort to integrate social robots into our daily
lives and environments. Despite this, we argue that this does not draw a complete picture of trust
relationships between humanity and automation. In fact, there is evidence that trust is a bidirectional
relationship and that the dynamics of mutual trust should not be ignored [13].
To address this gap in the current knowledge, we have performed a systematic review of the current
literature, searching for scientific publications that describe computational models of CT in HRI. Our
investigation across three major scientific databases (IEEE Xplore, Scopus, and Web of Science) has led
to the selection of 101 papers. By analysing them, we have generated a map of the co-occurrence of
their keywords, reported in Figure 1. In its upper branch, the keyword “robot trust” appears, which is
very closely linked to CT. Not only it appears in very recent publications, but it derives from topics
such as “decision making” and “cognitive model”. This is evidence of a recent trend in the literature to
embed trust within robotic agents. However, this term is ambiguous, as it is mainly used to depict the
classical perspective of human trust in robots. To avoid any confusion, we should instead refer to the
more objective term of CT, which unambiguously refers to any way of mathematically modeling trust
estimates in HRI. Furthermore, the keyword “human-robot collaboration” is closely linked to “trust”.
Finally, it is worth noting that, since we looked for CT models, it emerges that these models are
increasingly being used to modulate the behaviour of robots during collaborative tasks with humans.
All this evidence underscores the importance of pursuing standards in designing CT models.
Acknowledgments
Francesco Semeraro’s work was supported by the UKRI DTP CASE-conversion “Human-Robot Col-
laboration for Flexible Manufacturing” (Ref. 2480772), sponsored by UKRI Engineering and Physical
Sciences Research Council and BAE Systems plc.
Marta Romeo’s work was supported by the UKRI Node on Trust (Ref. EP/V026682/1, https://trust.tas.
ac.uk).
Angelo Cangelosi’s work was partially supported by the Horizon projects PRIMI, MUSAE and the
ERC Advanced eTALK (funded by UKRI) and the UKRI Trustworthy Autonomous Systems Node on
Trust (Ref. EP/V026682/1).
Samuele Vinanzi’s work was partially supported by Sheffield Hallam University’s Early Career
Research and Innovation Fellowship. This material is based upon work supported by the Air Force
Office of Scientific Research, Air Force Materiel Command, USA. Funder award Ref. FA9550-19-1-7002.
For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) license
to any Author Accepted Manuscript version arising from this submission.
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