Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 4 (March 2018) 295–298___________________________________________________________________________________________________________

Trust of Virtual Agent in Multi Actor Interactions
Halimahtun M. Khalid

Damai Sciences, A-31-3 Suasana Sentral, Jalan Stesen Sentral 5,
504700 Kuala Lumpur, Malaysia
Liew Wei Shiung

Faculty of Computer Science and Information Technology, University of Malaya,50603 Kuala Lumpur, Malaysia
Voong Bin Sheng

Damai Sciences, Suite R26-11 Dua Sentral, No. 8 Jalan Tun Sambanthan,50470 Kuala Lumpur, Malaysia
Martin G. Helander

Damai Sciences, A-31-3 Suasana Sentral, Jalan Stesen Sentral 5,
50470 Kuala Lumpur, Malaysia
E-mail: mahtunkhalid@gmail.com, liew.wei.shiung@gmail.com, sh3ng527@gmail.com, mahelander@gmail.com

Abstract
Trust is crucial when integrating virtual agents in human teams. Our study investigated the combined use ofpsychological and physiological measures in predicting human trust of agents undertaking social tasks. The
psychological measures comprised trust scores on ability, benevolence and integrity. The physiological measures
included facial expressions, voice, heart rate and gestural postures. Subjects interacted with two avatars. A neurofuzzy algorithm extracted rules from the psychophysiological data to predict trust. Results revealed that trust can bepredicted with 88% accuracy.
Keywords: Trust, virtual agent, measurement, human-agent interaction.

actor and bilingual contexts. In this instance, the role ofthe humanoid agent or avatar was to mediate between ateleoperator and clients using communication dialog ineither English or Bahasa Melayu (Malay language).
Therefore, the main objective of our study was toestimate human trust of the avatar in a business mediation
context. We hypothesized that trustor’s (evaluator) trustof the trustee (agent) is influenced by factors of gender,
ethnicity and trust components.

1. Introduction
Trust is a key element in the development of effective
human-agent-human relationships, as trust affects systemeffectiveness of safety, performance, and usability. With
the development and integration of virtual agents inhuman teams, the issue of predicting trust has become afocal concern1. One of the gaps in research is the lack ofa reliable measure of human-robotic trust. Past studies
have emphasized subjective measurements only2. We
present a method where subjective (general trust,
psychological) measures and objective (physiological)

2. Method
In this section, we describe the study as follow.

measures were mapped to predict human trust of
humanoid agents in performing social tasks in a multi-

Copyright © 2018, the Authors. Published by Atlantis Press.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 4 (March 2018) 295–298___________________________________________________________________________________________________________

2.1. Subjects and location

B. The experimenters and the server for recording data
were positioned in an observation room.

Forty-eight subjects (25 males and 23 females) aged
between 21 to 46 years (mean age 26 ±5 years)
participated in the study. Twenty-two of the subjects
were Malays and 26 were Chinese. To achieve ecologicalvalidity the experiment was conducted in an office settingof a R&D studio.

2.3. Measures and ROS integration
We measured trust subjectively using criteria of: (a)General Trust and (b) Psychological Trust. General Trustis made up of three factors: Ability, Benevolence andIntegrity (ABI)3. For each factor there were four
attributes presented as semantic differential word pairs.
Altogether there were 12 attributes in the General Trust
scale as summarized in Table 1. These attributes werereplicated from previous studies4,52.2. Avatars and equipment
Two avatars were designed for the experiment. One
represented a male Chinese and the other a female Malay
using humanoid characteristics derived from an earlier
study. Figure 1 illustrates the avatars.

Table 1. General Trust as defined by three main categories,
each consisting of four polar attributes
Trust Category
Positive
Negative
Competent
Incompetent
Knowledgeable Not Knowledgeable
ABILITY
QualifiedUnqualified
SkilledUnskilled
Cheerful
Not Cheerful
FriendlyUnfriendly
BENEVOLENCE
Kind
UnkindPleasant
Unpleasant
Dependable
Undependable
EthicalUnethical
INTEGRITY
HonestDishonest
Reliable
Unreliable

Fig. 1 Male and female avatars

Microsoft Kinect V2 technology was used to record theaudiovisual data, and a computerized scale was used torecord subjective measures. Data was logged directly into
a server via a gigabit router and Ethernet cables, whichconnected the server to the laptops used by the subjects.
Headset microphones were used to record voices.

The above attributes in Table 1 were embedded in theinteractive dialog.

Fig. 2. Arrangement of experimental session

Figure 2 shows the layout of the experimental session,
which comprised of 3 rooms. Trustor A (Teleoperator)
was in Room A and Trustors B and C (Clients) in Room

Data collection and analysis were made possible via the
Robot Operating System (ROS) framework, which
integrated the agents, Graphical User Interface (GUI),

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Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 4 (March 2018) 295–298___________________________________________________________________________________________________________and hardware. The audiovisual and evaluation data wererecorded on the server computer acting as ROS master.
The avatar was controlled by a trigger signal sent bysubjects through the GUI.

The experimental design was a within-subjects design
with manipulations of gender, ethnicity, language andcompliance to instructions by teleoperator, clients andavatar.
The dialog comprised a communication script of aBusiness scenario that concerns establishing a factory.
This scenario was selected, as it was proven from theresults of previous studies to be the most effective than aDisaster or Healthcare scenarios1,2.

The objective data, which was obtained from the Kinectand headsets, were analyzed separately. The video, audio,
and evaluation recordings for each subject weresegmented based on the ROS timestamps corresponding
to the time when subjects evaluated each trust attribute.
The segments were then processed as follow: Voicerecordings were processed using PRAAT6 and Audio
Analysis Library7. Facial expressions were processedusing OpenFace8. Heart rate signals were estimated fromthe subjects’ faces using independent component
analysis9. Gestural postures were estimated from
Kinect’s joint coordinates. All extracted objective
features were then appended with contextual andsubjective information such as the trust evaluation score,dialog condition, and the avatar type.

2.5. Procedure

2.7. Trust classification

2.4. Experimental design and dialog

To predict trust, we mapped the objective features fromthe facial expressions, heart rate variability, voice, andgestures10 of subjects to their subjective scores, to create
a dataset. A neurofuzzy classifier ensemble method wasthen used to learn and predict subjects’ trust of the virtual
agents.

First, subjects were briefed on the experiment and theirtasks. Second, they signed an informed consent form andcompleted a subject profile sheet. Third, they weretrained on the GUI to familiarize them with the online
scoring method and dialog box.
Testing was done in a group of three subjects. The
subjects were randomly assigned to the role of Operator
or Clients based on a draw lot technique. Typically, theteleoperator in Room A initiated the interaction, followedby the robot confirming its task, and interacting with
Trustor B and Trustor C in Room B, who then responded
to the avatar, which in turn communicated back to theteleoperator. Subjects were instructed to act out the scriptin their respective roles. To test for systems operability
and usability, a pilot study with two groups of subjects
was carried out prior to the actual experiment.
Experimenters were positioned in a separate observation
room and monitored the experiment via a screen and aheadset. The computer server was placed in theobservation room to record data in real time from theKinects, headsets, and subject evaluation forms.
After completing each application scenario, subjects
were given a 10-minutes break to complete a post-hoc
questionnaire, which assessed their opinions andpreferences regarding the avatar and its features.

3. Results and Discussion
The results from MANOVA and correlation analyses
revealed the following.
3.1. Gender and ethnicity
The gender and ethnicity of the trustee (virtual agent) had
no significant effect on trust. But the interaction effects
of gender and ethnicity of the trustors (human subjects)
influenced their evaluations of the trustees, with F (1,2304) =18.02, p<.001. Gender-wise, female subjects
were more trusting, giving higher trust scores than malesubjects, F (1,2304)=24.69, p<.001. This finding
confirmed our hypothesis on the influence of gender andethnicity in trust evaluations.
However, ethnicity alone had no significant effect. Itsinteraction with trust components produced a significant
effect, F (2, 2304)=5.01, p=.007. On average, Chinese
subjects scored the agents highly on ‘Ability’ and‘Integrity’ attributes, while Malay subjects scored theagents highly on the ‘Ability’ attributes only.
In addition, there were significant effects of intraethnicity on trust, with F (2, 2304)=4.17, p=.016. When

2.6. Data analysis
We performed factor analysis, MANOVA and Pearson
Correlations on the subjective data to test the hypotheses.

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Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 4 (March 2018) 295–298___________________________________________________________________________________________________________

the groups consisted of just Chinese subjects, theevaluation of trust was higher than when the groups
consisted of Malays only, or when it represented a mixedethnic composition. However, the gender composition of
the group had no significant effect on trust evaluations.
This finding suggests the importance of cultural andethnic composition of teams in trust-related tasks.

Acknowledgment

3.2. Subject preferences

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We gratefully acknowledge the financial support by the
US Aerospace Research and Development office(AOARD), Japan and the US Air Force Office of
Scientific Research (AFOSR), Washington D.C. underGrant No. FA2386-14-1-0016.
References

An analysis of the post-experiment questionnaire
revealed that subjects’ trust evaluation scores correlated
significantly with their explicit Trust, r=0.09, p<.001,
and Liking preferences for the agents, r=0.06, p=.006.
Subjects who trusted one or more agents explicitly scoredhigher than subjects who explicitly trusted neither agent,
F (3,2304)=7.81, p<.001.
In addition, subjects who explicitly liked the avatars
scored highest; subjects who explicitly disliked theavatars scored second highest, and subjects who were
undecided scored lowest, F(2,2304)=3.681, p=.025.
3.3. Trust prediction
Using the neurofuzzy classifier ensemble method 10, wewere able to predict subject’s trust in humanoid agents,
with an accuracy of 88.3% + 0.2% (F1-score 0.8401),
when all physiological data was used. Greater accuracy
at 90% was achieved when gestural posture data was
excluded, whether for Low, Medium, or High Trust.
Details of the analysis are described elsewhere10.
4. Conclusion
This study has shown that virtual agents can be trusted toperform social tasks using communication dialogs in a
multi-actor and bilingual contexts. The trust by humans
varies with the gender and ethnicity of the evaluator. Also,
trust can be easily evaluated for attributes of ‘Ability’ and‘Integrity’ using dialogs than for ‘Benevolence’. Thisimplies that our design and modeling of the humanoid
agents requires further anthropomorphism to appeal tohumans, persona-wise.
Integration of the various data via ROS allows for realtime annotation of the data recordings. This enables
segmentation of the relevant objective data to each trustscore evaluation. As such, the accuracy of the neurofuzzyclassifier was enhanced to predict trust.

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