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Just follow the suit! Trust in Human-Robot Interactions during Card Game Playing

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Robots are currently being developed to enter our lives and interact with us in different tasks. For humans to be able to have a positive experience of interaction with such robots, they need to trust them to some degree. In this paper, we present the development and evaluation of a social robot that was created to play a card game with humans, playing the role of a partner and opponent. This type of activity is especially important, since our target group is elderly people-a population that often suffers from social isolation. Moreover, the card game scenario can lead to the development of interesting trust dynamics during the interaction, in which the human that partners with the robot needs to trust it in order to succeed and win the game. The design of the robot's behavior and game dynamics was inspired in previous user-centered design studies in which elderly people played the same game. Our evaluation results show that the levels of trust differ according to the previous knowledge that players have of their partners. Thus, humans seem to significantly increase their trust level towards a robot they already know, whilst maintaining the same level of trust in a human that they also previously knew. Henceforth, this paper shows that trust is a multifaceted construct that develops differently for humans and robots.
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Just follow the suit!
Trust in Human-Robot Interactions during Card Game Playing
Filipa Correia1, Patr´
ıcia Alves-Oliveira2, Nuno Maia1, Tiago Ribeiro1,
Sofia Petisca1, Francisco S. Melo1and Ana Paiva1
Abstract Robots are currently being developed to enter our
lives and interact with us in different tasks. For humans to
be able to have a positive experience of interaction with such
robots, they need to trust them to some degree. In this paper,
we present the development and evaluation of a social robot
that was created to play a card game with humans, playing
the role of a partner and opponent. This type of activity is
especially important, since our target group is elderly people - a
population that often suffers from social isolation. Moreover, the
card game scenario can lead to the development of interesting
trust dynamics during the interaction, in which the human that
partners with the robot needs to trust it in order to succeed
and win the game. The design of the robot’s behavior and game
dynamics was inspired in previous user-centered design studies
in which elderly people played the same game. Our evaluation
results show that the levels of trust differ according to the
previous knowledge that players have of their partners. Thus,
humans seem to significantly increase their trust level towards
a robot they already know, whilst maintaining the same level of
trust in a human that they also previously knew. Henceforth,
this paper shows that trust is a multifaceted construct that
develops differently for humans and robots.
I. INTRODUCTION
According to the World Population Prospects (http:
//esa.un.org/unpd/wpp), the United Nations envi-
sions that the world population will dramatically age in
the next few years. As such, the society needs to embrace
this transition and develop ways to deal with it. Moreover,
the elderly population commonly has physical or cognitive
impairments, and current technology offers a possibility to
deal and contribute to the Quality of Life (QoL), leading
to successful aging[1]. In particular, assistive robots play a
significant role in the technological evolution, as they could
eventually be capable of providing elderly care.
However, QoL goes beyond meeting health care needs to
address enjoyable and quality time, ultimately related with
satisfaction towards ones life[2]. Therefore, when dealing
with aged people with no serious health problems, that are
still capable of doing their regular daily tasks, there is still a
need to invest in their QoL, providing ways to occupy their
free time with entertaining activities.
Our previous research [3] has explored the activities in
which independent-living older adults require a robot. A
panoply of different activities that they can still do by their
1Filipa Correira, Nuno Maia, Tiago Ribeiro, Sofia Petisca,
Francisco S. Melo and Ana Paiva is with INESC-ID and
Instituto Superior T´
ecnico, Universidade de Lisboa, Portugal
filipa.correia@gaips.inesc-id.pt.
2Patr´
ıcia Alves-Oliveira is with INESC-ID and Instituto Universit´
ario de
Lisboa (ISCTE-IUL), CIS-IUL, Lisboa, Portugal
own, but with some degree of difficulty, translates their
need for assistance. Moreover, elderly people recurrently
expressed problems related to social isolation and a need to
reconnect. To meet this requirement, this paper presents the
development and evaluation of a robotic game partner and
opponent in a classical card game, which most elders enjoy
playing. The main aim of this research was to develop an
entertaining activity targeting the elderly population, using a
social robot to help reconnecting people.
The card game scenario is an entertainment scenario in
which three people play the game with a robot. This scenario
is part of the PArCEIRO project1, whose purpose is to
study the role of social robotic players during tabletop card
games with humans. The chosen card game was Sueca2,
since it is composed of two teams that simultaneously allow
a partnership between two human players and between a
human and a robotic player. This means our robotic game
player can sustain two roles during the game: partner and
opponent. This game in particular is one of the most played
games among the elderly population in Portugal.
The Artificial Intelligence (AI) for some complex games
has become very strong over the years. In fact, it has already
defeated human world champions, e.g. Deep Blue, Chinook
and Watson [4], [5], [6]. However, the increasing competence
of these artificial players, may lead humans to generally
consider them as fierce competitors. Yet, when we consider
games played in social environments or when they require
the AI to play as a social partner, people may still be wary
of trusting AI to be up to human standards.
To analyze the performance of this scenario and how joyful
participants felt, we conducted two user studies: one in a
controlled lab environment, and another into-the-wild. We
were interested in measuring the trust levels that participants
felt towards the robot as a partner, and compared them to the
trust felt towards human partners. Moreover, we analyzed the
positive and negative affect after the study, and compared it
with their baseline level. Finally, we studied the usability of
our system and how expert Sueca players feel when they
interact with it.
II. REL ATED WORK
A. Elderly and Robots
Several projects have been investing in robotic technology
to enhance QoL and successful aging, such as the ACCOM-
1http://gaips.inesc-id.pt/parceiro
2https://en.wikipedia.org/wiki/Sueca (card game)
25th IEEE International Symposium on
Robot and Human Interactive Communication (RO-MAN)
August 26-31, 2016. Columbia University, NY, USA
978-1-5090-3928-9/16/$31.00 ©2016 IEEE 507
PANY project3, CARE4, ENRICHME5, ExCITE6, Giraff-
Plus7, HOBBIT8, RAMCIP9, Robot-Era10 and SILVER11.
Moreover, Broekens et al. (2009), have reviewed robotic
technologies in elderly care and have emphasized the positive
effects of social assistive robots [7]. These kind of robots
may vary from a service type to a companion type. The
first ones are essentially focused on enhancing QoL aging of
independently living elders. On the other hand, companion
robots are also being used with therapeutic purposes.
In the service type, robots such as Pearl[8], Care-O-
bot II[9], RoboCare[10] present many similarities regarding
the guidance through environments and the management of
elders’ everyday activities. Their differences reside in their
sensors and the interface of communication with the users.
Overall, these robots were developed to provide home assis-
tance for elders with an independent living or to complement
caregivers’ support.
In the companion type, the Paro robot was used in a one-
year study with elderly participants possessing different lev-
els of dementia and revealing increases on their moods and
depression levels. Another example is the Huggable robot
that was developed to accompany patients in hospitals. Both
robots present extremely reactive functionalities regarding
touch and voice inputs, and their primary goal is therapeutic.
Indeed, technology seems to be perceived as helpful for
the elderly population, both for assistive purposes as well as
for entertaining activities.
B. Entertainment Robots
Game playing scenarios are rich environments to develop
human-robot interactions and the usage of social and emo-
tional robots has been regarded as more entertaining and
enjoyable when compared to virtual characters [11]. Leite
et al. (2009), developed a robotic chess tutor for children
and have analyzed how its social and empathic behaviors
can improve children’s engagement during the game [12].
Another social robotic game player is EMotive heaY systeM
(EMYS) the Risk player that was used to improve social
presence of an artificial opponent in the board game [13].
The role of robots in entertaining activities seems to have
its importance, and more work needs to be developed to
increase the usage of robots as a tool that re-connect people
and provide joyful moments. In this paper, we developed
an entertaining scenario in which elderly people and robots
meet to play a classic card game.
C. Trust in Human-robot and Human-Human Relationships
According to Hanook et al. (2011), a human must trust a
robot when interacting with it to have an effective usage of
3http://accompanyproject.eu/
4http://care-project.net/welcome/
5http://www.enrichme.eu/wordpress/
6http://www.aal-europe.eu/projects/excite/
7http://giraffplus.eu/
8http://hobbit.acin.tuwien.ac.at/
9http://www.ramcip-project.eu/ramcip/
10http://www.robot-era.eu/robotera/
11http://www.silverpcp.eu/
its capabilities, and to accomplish a common goal between
them, in the case of a human-robot team. The authors
conducted several experiments to examine which factors
influence the trust measure in this type of relationship, and
their studies revealed that trust in human-robot interaction is
a constellation of three factors: human-related; robot-related
and environmental. However, human-related factors (e.g.
attentional capacity and personality traits) and environmental
(e.g. task type and culture) presented a moderate effect
on trust, whilst robot-based factors, especially performance-
based, influenced the most trust towards a robot [14].
Thus, trust appears as a complex construct, especially
linked to the robot’s performance. However, trust in human-
human relationships appears also as a complex construct with
variables related to those of human-robot trust. Indeed, the
actions of the other seem to contribute to the trust we deposit
in one another. This is then related with the performance of
the other, e.g., the type of decisions he/she performs, etc.
Moreover, trust in human-human relationships is connected
with the recognition of positive expectations for the other,
despite of the inherent uncertainty. This includes cognitive,
behavioral and affective states that we expect the other to
have according to a given situation [15].
In this paper we have considered trust as a construct that
informs us about the quality of the human-robot interaction
in comparison with human-human interaction.
III. DESIGN
The goal of the aforementioned scenario was to create an
autonomous robot that is able to play the Sueca card game
on a touch table, and socially interact with its partner and
its two opponents in context of the game. To achieve this
goal, the design involved two different concerns: how can
the social robot behave in a human fashion during the game
(Section III-A); and how can the game interface handle the
interaction between humans and a robot while respecting the
usual game dynamics of Sueca (Section III-B).
A. Behavior Design for our Social Robot
According to Braezeal (2003), the robot’s sociability in-
creases with the ability to support a social model adapted
to the environment [16]. As a result, we conducted a user-
centered study to understand how human players behave
during Sueca games, and to further include those behaviors
in the design of our social robot.
The user-centered study took place in an Elder Care
Center, where participants were told to play Sueca for as long
as they wanted. The four male participants played 10 games
during about 30 minutes and their performances were audio-
and video-recorded for further behavioral analysis. Figure 1
illustrates participants setup during the user-centered study.
The behavioral analysis of the videos allowed us to obtain
a list of game events that contains specific moments during
a game where participants changed their previous behavior
or interacted with other players. Moreover, we collected
their verbal and non-verbal behaviors for each corresponding
game event. We have also observed that the same game event
508
Fig. 1: Elders playing Sueca during the user-centered study.
produces different behaviors according to who is doing it,
i.e. self, a partner or an opponent. For instance, participants
frequently used an encouraging tone when talking to their
partners and a competitive tone to their opponents. The final
list of our social robot’s utterances was inspired by all the
collected behaviors from the user-centered study and some
examples can be seen in Table I. Additionally, the video12
presented in [17] illustrates the social performance of our
robotic game player, which was implemented on an EMYS
robot.
TABLE I: Examples of utterances of the social robotic player.
Game Event Utterance
Receiving
own cards
<gaze(ownCards)>Let’s see...
<glance(dealer)>
Cutting
the trick
<gaze(opponent1)>Although I don’t know where
the ace is, <glance(opponent2)>I will cut
<glance(playingCard)>this one!
An opponent
cuts the trick
<gaze(partner)>What a bad luck... Look partner,
<glance(table)>he cut it! <glance(opponent)>
B. Entertainment Activity
In the previously mentioned user-centered study, we could
also analyze the game flow of human players playing Sueca
in the traditional scenario, and create the main usability
requirements for the game interface of our scenario.
Sueca is a popular and traditional Portuguese card game
among several age groups, including the elderly. Hence,
its players are accustomed to a very specific game flow
and speed, as well as the touch and feel of holding the
cards. Furthermore, since it is a team game, some partners
might even reach an intimacy level where they imperceptibly
cheat through gestures, looks or moves, which confirms the
engaging game experience some players are used to.
We have noticed participants attached great importance to
their cards during the game. Firstly, they had to hold them
in a way nobody else could see them. Secondly, their hands
were at the locations they have most frequently looked at,
which we attributed as a sign of deliberation about their
next moves. As a result, our system had to use physical
cards at the same time as it provides a mechanism for the
robot to play and recognize the others plays. This usability
requirement might be granted using a multimodal interface
over a touch table that is capable of recognizing the cards,
e.g. using fiducial markers on cards.
Considering this approach, we also analyzed the location
that participants typically throw their cards over the table.
12https://vimeo.com/153148841
The relevance of this question consisted in creating a mech-
anism to solve the possible overlap between cards. However,
we have noticed that participants usually place their cards
in the center of the table, although as near as possible to
their location, so that other players can understand who has
played each card. Additionally, if, after throwing a card, it
overlapped another, participants have always adjusted the
cards position, which eliminates the overlapping problem.
IV. SYS TEM ARCHITECTURE
In order to simplify the development and integration of
our robot with the game, we use the SERA ecosystem[18],
as shown in Figure 2. The Thalamus system provides the
integration framework of all modules, Skene is the semi-
autonomous behavior planner that uses a high-level language
to manage the robot’s behaviors, and Nutty Tracks is the
robot’s animation engine.
Fig. 2: The architecture of the Sueca-playing robot.
The Decision Maker Module represents our robotic agent
in this system, and is divided into two modules, one for
each of its main tasks, i.e. to compute the game and to
prescribe social behaviors. Nonetheless, these two modules
are regularly communicating with each other in a symbiotic
manner to combine their outputs in a proper way. An example
that illustrates this cooperational concept between the two
modules is an opponent playing a card, which may trigger
a behavior associated to the game event opponent play. At
the same time, that play is computed in the current game
state of the agent to calculate the benefit it produces for its
team and that value can even be mentioned by the robot in
a sentence and be used to update its emotional state. This
emotional state is used in the Decision Maker Module and is
produced by FAtiMA, the emotional agent architecture[19],
in order to update the robots behaviors and posture.
When the AI Module has to choose a card to play, it uses
the Perfect Information Monte-Carlo (PIMC) algorithm in its
deliberation process. This algorithmic approach has obtained
remarkable results in similar AIs for hidden information
trick-taking card games, e.g. Bridge[20] and Skat[21].
The Sueca Game Module provides the interface, game
engine and is also responsible for the physical cards recog-
nition. Our deck had to be redesigned so that each card
can include fiducial markers and, therefore, can be detected
by the touch table13 using the recTIVision framework14,
see Figure 3. The robots physical cards are recognized at
13https://www.multitaction.com/
14http://reactivision.sourceforge.net/
509
the beginning and then virtually played during the game.
In this recognition phase, the cards are placed facedown,
which justifies the need to include forwards a fiducial marker.
Figure 4(a) illustrates the usage of the cards either by the
robot and the human players.
Fig. 3: Standard french deck card, on the left, and our
redesigned card with a fiducial marker, on the right.
Nonetheless, the usage of physical cards also brought
some limitations. Firstly, the differences between our version
of cards and the traditional ones were pointed out as confus-
ing and some players have sometimes played incorrectly due
this misunderstanding. The purpose of the black background
is to contrast with the white maker contour, and the more
isolated the marker is, the better it will be recognized. The
second limitation is the markers’ recognition, which failed
in 4 out of 100 games.
V. ST UDI ES
Two different studies were performed with the Sueca
scenario: a lab study (see Section V-A) to analyze the trust
levels of participants when partnering with a robot or a
human during the card game; and an into-the-wild study (see
Section V-B) in which we deployed our scenario in a Sueca
tournament, providing an opportunity to test the game-play
of this scenario with users that are expert Sueca players.
A. Lab study
This study was run in a lab and participants were adults
who volunteered for the experiment. Although the target
group for this scenario is elderly people, it is required to
test the system before to understand how it is performing
in a controlled lab setting. To do this, we conducted a lab
study with the goal being to test if the scenario is stable
enough. As Sueca is a partnership game, the aim goal of
this study was to analyze the trust levels that humans feel
towards their human or robotic game partners. As robots are
a type of technology that usually triggers a novelty effect,
we thus target the study of trust levels according to previous
knowledge that participants had on their game partners
(either humans and robot). This was possible, as some of the
recruited participants participated in previous studies with
the EMYS robot, and thus, had already interacted with it.
In a similar way for the human-human partnership, some
participants knew each other before playing the game, while
others were strangers.
1) Procedures and Methodology: Each session involved
three participants playing Sueca with the EMYS robot and
lasted about one hour. At the beginning, participants’ part-
ners were selected in a draw so that one of them would
be the robot’s partner and the other two would be each
other’s partner. Before the game-play, they answered to a
questionnaire to assess their affect (PANAS Questionnaire
[22]), and the Human-Robot Trust Questionnaire [23] with
an adapted version for participants with a human partners.
The Human-Robot Trust Questionnaire measures trust in a
scale ranging from 0% of trust to 100% of trust. We aimed
to measure participants emotional state before the game, and
also their trust expectation towards their partners. To have
a standardized version of the game during the study one
researcher explained the game rules and played Sueca with
the three participants using the traditional french deck, before
they started to play with the robot. Afterwards, participants
were invited to play the game with the EMYS robot in a mul-
titouch table, where the three participants played a session
of five games with the robot (see Figure 4(a)). At the end of
the five games, they answered to the post-questionnaires of
PANAS, the Human-Robot Trust Questionnaire, and to some
demographic questions. The goal was to compare the trust
levels of participants towards the robot or the human partner
according to their previous knowledge of the same partners,
i.e., if it was a first interaction with them or if they already
had interacted with each other.
2) Sample: This study included 60 participants (M=24.31,
SD=3.852; 20 females, 39 males, 1 unknown). 20 participants
had EMYS as their game partner, while 40 had a human
partner during the experiment. The majority of participants
classified themselves with a medium level of proficiency
in the Sueca game. We measured the robot’s performance
during the game to assure that the robot’s ability to play did
not interfere with the trust levels that participants felt towards
it. Henceforth, its performance was measured according to
the percentage of won and drawn sessions by its team. They
won 12 sessions out 20 (60%) and drew 1 session (5%). This
led us to conclude that the robot performed well during the
game and showed a good playing ability.
3) Trust Level in the Game Partner: The trust levels
were analyzed according to two factors: the partner type
(human vs. robot); and the partner knowledge, i.e., the
level of previous interaction with the assigned partner (in
the demographics questionnaire this was controlled using
the following statements: “I have never seen my partner
before” vs. “I have already interacted with my partner”.
We used a Mixed ANOVA statistical test to analyse if the
aforementioned factors influenced the trust levels felt by
the participants towards their partner. Results presented a
significant difference between the trust levels before and after
playing the game according to each possible partner type and
partner knowledge (F(1;49)=7.093, sig=.010). Thus, when
analyzing the participants that had no previous interaction
with their partners before the game, we can see that those
who partnered with a human seem to increase their levels of
trust on their partner (74.50% to 81.47%) when compared
to participants who partnered with the robot (66.38% to
65.64%) (see Figure 5. (a)). When analyzing the results for
the participants that had already interacted with their game
partner before, we can see the emergence of a different
pattern. In fact, participants who partnered with a human
510
(a) (b)
Fig. 4: Participants playing the Sueca card game with the EMYS robot during (a) the lab study and (b) the Sueca tournament.
that they had already interacted with, showed equivalent level
of trust on their partner (79.86% to 81.14%) (see Figure 5.
(b)). Conversely, participants who had EMYS as their partner
in the game and that had already interacted with it, show
an increase on their trust level (from 61.39% to 70.37%).
Moreover, the level of trust in human partners always appears
to be higher than of the trust level in the robot.
(a) (b)
Fig. 5: Trust towards a human partner and a robotic partner
according to the previous level of interaction with the partner,
none in (a) and high in (b).
4) Affect: The PANAS Questionnaire specifies the emo-
tional state into two dimensions: the positive affect and
negative affect scales. We run a Mixed ANOVA statistical test
on data and the results showed that the positive affect signifi-
cantly increased when compared the affect before (M=29.77;
SD=6.84; M=31.35; SD=8.11, for human and robot partners)
and after (M=32.80; SD=7.75; M=33.15; SD=9.16, for hu-
man and robot partners) playing the game, F(1;58)=7.564,
sig=.008, with no significant difference between conditions,
F(1;58)=.488, sig=.488. These results shows that indepen-
dently of having a robotic or a human partner in the game,
participants felt with higher positive affect values after the
interaction, revealing that the entertaining scenario triggers
positive affect states in the players. When looking at the
negative affect, results do not present significant differences
before (M=11.48; SD=2.18; M=13.35; SD=4.25, for human
and robot partners) and after playing the game (M=12.58;
SD=2.98; M=13.25; SD=4.15, for human and robot partners),
F(1;58)=1.257, sig=.267. Moreover, there was no significant
difference for the negative affect before and after playing
the game between different partner types F(1;58)=1.810,
sig=.184. This shows that the negative affect did not increase
after playing the game with the robot.
B. Into-the-wild study
This study was conducted during a Sueca tournament,
where we aimed to examine different users interacting with
the system, possibly including proficient Sueca players. For
this experiment, the set-up was placed in a formal Sueca
Tornament that occurs every year in a Lisbon area (see
Figure 5. (b)).
1) Sample: The session lasted about 2 hours and the 15
subjects played 13 games with EMYS. Each group of three
participants had played between one or three consecutive
games. Then, participants and some members of the audience
were asked to answer a questionnaire about their opinions
related to the robot and the game experience using the
multitouch table to play a classical card game. Thus, 15
participants and 2 members of the audience answered the
questionnaire (M=22.62 years old, SD=10.73; 2 female, 14
male, 1 unknown).
2) Results: EMYS performance was evaluated using three
multiple-choice questions:
Question 1: “How well did EMYS play?”
Question 2: “Which kind of mistakes did it commit?”
Question 3: “Does EMYS play like a human player?”
Results show that EMYS’ plays were mainly classified as
“It always played well” (70,6%), and participants reinforced
this idea in the second question by mainly answering “It
always played well” (75%). In the third question, the mode
of the answers was “It is similar to a human player, although
with some differences” (80%). Secondly, we tried to evaluate
their perception of the game in terms of usability, considering
it was a new experience playing a card game with phys-
ical cards over a multitouch table. The questionnaire also
included two questions related with game dynamics using
this type of technology:
Question 4: “Did you like to play/watch the game over
the touch table?”
Question 5: “Which problems do think are relevant
about this experience over the touch table?”
The majority of the participants answered that they “loved
the experience” (64,7%). For question 5, although 25% found
that “There were no problems”, the remaining answers were
spread between “Sometimes the table takes too long to
recognize the cards” (30%) and “The game flow is not
natural” (35%). Interestingly, the Sueca champions did not
want to play with EMYS. As this was a curious behavior,
511
we talked to a few of them, and they answered they are “not
willing to lose their reputation by losing with a robot..
VI. DISCUSSION AND CONCLUSION
In this paper we presented the development and evaluation
of an entertainment scenario with a robot. The underlying
motivation of this work was to meet (some of the) needs
of the elderly population related with social isolation. This
paper shows the design process of the scenario and the
robot’s behavior, as well as its evaluation in a controlled lab
study and in a real-world context. As this scenario is about
a card game in which people need to team up with their
partners to beat their opponents, we measured the level of
trust felt by participants towards their human/robot partner.
We conclude that humans do trust a robot as a partner, but
the trust level varies according to their previous knowledge
of interaction with the same robot. Thus, participants that
had already interacted with the EMYS robot, increased
their level of trust after the game more than participants
that had already interacted with human partners. However,
participants without previous knowledge of their robotic
partner did not increase their trust levels, suggesting that
the development of trust towards robots may need longer
interactions. These findings assist previous theories of trust,
in which this concept appears as a complex construct.
Indeed, trust in robots appears to be directly associated with
performance, and since the robot had a good performance
in terms of playing the game, humans trust it to be their
partners. As for humans, trust between them seems to be
linked not only with performance but also with expectations
towards their behaviors, cognition and emotions, increasing
the complexity of this concept. In line with this, trust between
humans involves more than playing a game with them.
This scenario was also tested in the real world with the
into-the-wild study, which suggested a successful perfor-
mance of the social robotic autonomous partner in the Sueca
card game for an uncontrolled environment.
Overall this study shows the success of implementing a
social robot as a partner in a card game scenario, which is
technically stable and reliable to be further tested with the
elderly people. Therefore, our future work is to understand
the impact this can have in their QoL.
ACKNOWLEDGMENT
This work was supported by national funds through
Fundac¸˜
ao para a Ciˆ
encia e a Tecnologia (FCT) with refer-
ence UID/CEC/50021/2013. P. Alves-Oliveira acknowledges
a FCT grant ref. SFRH/BD/110223/2015 and T. Ribeiro
FCT grant ref. SFRH/BD/97150/2013. The authors are solely
responsible for the content of this publication. It does not
represent the opinion of the EC, and the EC is not responsible
for any use that might be made of data appearing therein. The
authors show their gratitude to Centro de Dia da Santa Casa
da Miseri´
ordia de Lisboa and to Par´
oquia de Santa Teresinha
for their involvement in the studies.
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... The results suggest an inverse proportional relationship between cognitive load, measured by the change of pupil size of the participants and trust. Correia et al. [9] designed a card-playing game in which human card players were partnered with robots and humans. In this study, the authors focused on showing whether the level of trust can be developed differently for humans and robots. ...
... We note that our proposal is also well aligned with the studies that aim at determining the trust factor of humans in artificial agents. For instance, the studies that we introduce in Section II also emphasize that cognitive load and emotion are two potent elements for humans to establish trust in artificial and biological agents [6], [7], [8], [9]. ...
... Other research has shown similar context effects during studies of robots engaged in physical game playing [20,21], as well as effects from robot group size [22]. The contextual factors in those cases included not only the physical embodied form of the robots, but also their behaviors and ability to elicit trust/empathy from human interactors in order to accomplish shared goals. ...
... Indeed, the component capabilities are deeply interlinked to the characters' cooperative actions and game session evolvement-a known pre-requisite for creating a successful interactive conversational agent [57]. This situatedness demands an empirical (in our case data-driven) design approach, which aligns with best practices for designing immersive voice interaction [21]. It also highlights how that same process can be used to create customizable social environments to explore a broad range of hypotheses related to how contextual factors relate to people's perceptions of interactive technology. ...
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... Часть исследований ориентировано на применение моделирования эмоций для генерации человекоподобных реакций, что обогащает взаимодействие человека и искусственного агента [11,44]. Примерами таких проектов с искусственными эмоциональными агентами являются роботы-сиделки для ухода за пожилыми людьми [14] и приложения, которые помогают принимать сложные решения [25]. Целью теоретических исследований обычно выступает моделирование механизмов естественных эмоций для проверки гипотез о человеческом эмоциональном аппарате [29]. ...
... Архитектура. Многие когнитивные архитектуры, как и другие проектируемые системы, являются модульными [14,15]. Это связано прежде всего с тем фактом, что модульность относится к основным принципам инженерии. ...
... For example, some authors have shown that the elder may find the robot useful, but only for certain tasks [25] . Other present-day studies have attempted to assess the effect of a robot-committed error in trust, but they have considered contexts of mild severity of robot error, such as in card games [26] , Lego games [27] , robotic suitcase [28] or other simple domestic tasks (navigating the house, setting a table, playing music) [29] . Hence, it is imperative to consider highsensitivity tasks (e.g., health-related), for which the robot's success rate might have considerable implications on trust. ...
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... For example, there are works which aim to design models for robots to appropriately approach a human to initiate a conversation [3,4] and to maintain appropriate distance with a human during interaction [5]. With a recent shift of focus towards group interaction, however, new application areas have emerged, such as surveillance [6][7][8], playing games with groups of people [9,10], studying human behaviour in a group interaction [2,11], and tracking groups [12][13][14][15]; while relevant for understanding group dynamics, there still remains a gap in the research on how to ensure that robots are able to join an ongoing group interaction. Most of the works mentioned here do not use a robot in their experiments and some works which do use a robot consider the robot to be a part of the group. ...
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