Improving Active Attitude for Interactive Decision-making with Multiple

Agents by Increasing Personal Resource

Yoshimasa Ohmoto

, Masato Kuno

and Toyoaki Nishida

Department of Infomatics, Shizuoka University, Hamamatsu-shi, Shizuoka-ken, Japan

Graduate School of Infomatics, Kyoto University, Kyoto, Kyoto, Japan

Keywords:

Human-agent Interaction, Human Factors, Decision-making, Work Engagement.

Abstract:

When human participants collaborate in decision-making and problem solving, the results are often better than

those obtained from individual efforts. However, when they cooperatively perform tasks with an embodied

agent, the agent is often regarded merely as a human-centric multi-modal interface from which information is

obtained. In this study, by increasing the “personal resource” in the aspect of work engagement, we aim to

incorporate active attitude in the human participants toward task and in their interactions with the embodied

agents. We conducted an experiment to investigate whether increasing the personal resource will impact the

active attitude of participants. In the experiment, we used a mediator agent that increased either the “personal

resource” or “job resource” in addition to an expert agent that directly supported the task. The results suggested

that a mediator agent that increased the personal resources can induce the active attitude of participants in the

human-agent interaction.

1 INTRODUCTION

Agents capable of intelligent response are expected

not only to act as multimodal interfaces that provide

solutions to problems efﬁciently but also to facilitate

more creative activities by promoting human under-

standing and learning. When the problems are collab-

oratively solved, we expect that the results are often

better than those obtained from individual efforts.

In ICAP theory, in the framework of human co-

operative learning (Chi and Wylie, 2014), the activity

states of learners in cooperative learning are catego-

rized under the following four states: 1) Passive state:

passively learning from a teacher; 2) Active state: ac-

tively and voluntarily tackling the problem; 3) Con-

structive state: reconstruction of metacognition and

own knowledge through discussion with others; 4) In-

teractive state: reconstruction of knowledge through

criticism and refutation of others’ ideas. It was dis-

covered that the performance of the learners in coop-

erative learning increased as they transitioned to the

interactive state (I > C > A > P). When an agent

efﬁciently searches for a solution, and returns a re-

sponse to a human question, the human is in the pas-

sive state. When collaborating with agents on tasks,

the goal should be to attain the interactive state in the

ICAP theory. However, many cooperative agents have

not been able to effectively change the human state

from Passive to Active (Raux et al., 2005; Misu et al.,

2011). In order to induce humans to actively and vol-

untarily tackle problems through human agent inter-

action, it is necessary to elicit an active response from

humans to the agent’s interactions.

The main components of an agent that differ

from those of other systems are summarized by re-

searchers, such as (Wooldridge, 2009; Russell and

Norvig, 2002). From those, we consider that it is im-

portant for agents to demonstrate ”reactivity” to hu-

mans and ”social ability”. ”Reactivity” is the ability

to incorporate information from others into one’s own

behavior. ”Social ability” is the ability to assume that

others have intentions of sharing information and out-

comes. Meanwhile, to recognize these elements, and

induce an ”intentional stance,” it is important a hu-

man’s active attitude which tries to deduce the inner

state of an agent.

As we expect humans and agents to collaborate

to accomplish tasks, we focused on “work engage-

ment” as a form of active attitude in task execution.

Schaufeli et al. (Schaufeli and Bakker, 2004) de-

scribed high work engagement as a state of mind

associated with a positive, fulﬁlling sense of work.

Employees with a high level of work engagement

are enthusiastic about their work, and tend to regard

Ohmoto, Y., Kuno, M. and Nishida, T.

Improving Active Attitude for Interactive Decision-making with Multiple Agents by Increasing Personal Resource.

DOI: 10.5220/0010265503290336

In Proceedings of the 13th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2021) - Volume 1, pages 329-336

ISBN: 978-989-758-484-8

329

work as rewarding rather than stressful. Bakker et

al. (Bakker et al., 2004) demonstrated that employ-

ees with high work engagement were highly rated by

their colleagues for their performances in both obli-

gations and volunteer work.

Kotze (Kotze, 2018) highlighted two main types

of work engagement resources as follows. 1.) Job

resources: The characteristics of a job that facilitate

the realization of a job’s goals, and drive the growth

of those involved. 2.) Personal resources: A positive

self-esteem associated with an individual’s sense of

control and inﬂuence over his/her environment. Job

resource can be regarded as the resource that exter-

nally motivates a person, and personal resource as one

that internally motivates a person. Increasing the re-

sources enhances work engagement. We think that

job resources are related to “reactivity,” whereas per-

sonal resources are related to “social ability.”

The framework that focuses on work engagement

is based on studies concerning workers in a day-to-

day work environment. We considered the conver-

sation and interaction were regarded as basic activi-

ties (i.e., day-to-day work) for performing the task.

Therefore, in this study, we assume that by enhancing

work engagement in decision-making tasks, it is pos-

sible to increase the active tackling of a human toward

the task and interaction with an agent.

The purpose of this study is to investigate whether

the application of a work engagement enhancement

framework to a cooperative decision-making task

with agents can make human responses to agents

more active. Our ﬁnal purpose is to improve the at-

titude of people working on cooperative tasks with

agents such that they attain the interactive state in

the ICAP theory. The goal of this research is to in-

duce “Active” human attitudes in human-agent inter-

actions, which are the most fundamental elements of

cooperative decision-making, as a ﬁrst step in achiev-

ing this goal. A human attitude in performing tasks

can be either explicitly observed from one’s behav-

ior or implicitly embedded in it. Physiological in-

dices are measured to observe changes in human inner

states that are not easily inferred from the behavior

during human-agent interactions; subsequently, these

are used in addition to the explicitly expressed behav-

ior for evaluation.

The present paper is organized as follows. In next

section, we brieﬂy explains the multi-agent system

designed with the method to increase resources re-

lated to work engagement. In the Experiment sec-

tion, we presents the results of an experiment con-

ducted to investigate the effect of a speciﬁc behavior

of the agent that increased the personal resources. In

the Discussion section, the contributions of this study

and a future study are described. The conclusions are

presented in the Conclusion section.

2 CONSULTATION AGENTS TO

INCREASE JOB AND

PERSONAL RESOURCES

For humans to perform decision-making interactively

with an agent, some methods were developed to

induce the “intentional stance”(Dennett, 1988) by

presenting goal-oriented behaviors, expressing con-

tingent responses, and dynamically estimating the

user’s behavioral intentions in previous studies (e.g.,

(Ohmoto et al., 2017)). In these methods, the agent

enables the human to recognize its “social ability” by

demonstrating “reactivity”. Meanwhile, to maintain

the “intentional stance,” it is still important that a hu-

man attempts to actively estimate the internal state of

the agent.

In the tasks that humans and agents collabora-

tively accomplish, we focused on “work engagement”

as a form of active attitude in task execution. In con-

trast to previous studies, we investigate whether work

engagement, a type of “reactivity” to the agent’s ac-

tivities, is induced by the agent’s manifestation of “so-

cial ability.” Speciﬁcally, between “job resource” and

“personal resource” that have been proposed as re-

sources to enhance work engagement, we investigate

whether agents can improve humans’ active attitude

by revealing the agent behavior designed to increase

the “personal resource” related to social relationships.

2.1 Agent Behavior Related to

Increasing Resources

Job resources typically include interactions that facil-

itate task accomplishment and ability improvement,

such as peer support and performance feedback. Per-

sonal resources typically include interactions involv-

ing social relationships with others, such as enhancing

self-efﬁcacy and self-esteem.

The basic function of consultation agents is to pro-

pose and explain solutions, methods, and hints. This

implies that the consultation agent functions as “a

colleague who helps in decision-making” to increase

job resources. In other words, existing consultation

agents increase the job resources to stimulate human

interactions and tasks.

Some agents are designed to increase personal re-

sources. For example, studies that attempt to gener-

ate rapport between a human and agent (e.g., (Gratch

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

330

et al., 2007)) have been conducted. These agents at-

tempt to encourage human interactions and tasks by

improving social relationships.

To make a decision in an unfamiliar domain, an

expert’s opinion is needed. The expert contributes to

increased job resource. On the other hand, the behav-

ior to increase personal resource is sometimes conﬂict

the behavior to increase job resource. Therefore, con-

ducting an interaction with the goal of increasing per-

sonal resources using only one agent is challenging.

In this study, we thus used two types of agents: (i) an

expert agent with advanced knowledge of tasks; (ii) a

mediator agent who has only a general knowledge of

tasks. We also expect the participant in the interaction

to estimate the agent interaction model by observing

the interaction between the agents. This is similar to

the communication strategy used to persuade users ef-

fectively (Walster and Festinger, 1962; Andr

e et al.,

2000). Although we do not focus on persuasion in the

current study, it is useful for consultation interaction.

2.1.1 Role of an Expert Agent

The role of the expert agent is to answer user ques-

tions, and generate a proposal that will aid the user

(and a mediator agent) in decision-making. The ac-

tions performed by an expert agent are as follows:

• Provide responses for the user by employing ﬁller

and nodding motions.

• Provide responses for the mediator agent based on

the predeﬁned responses.

• Provide proposals, which are similar to the user

preferences, from a prepared list.

• Modify the contents of the proposals, and provide

variations that are similar to the user preferences,

from the prepared list.

• Explain three major factors included in the pro-

posals.

• Answer the user questions.

2.1.2 Role of a Mediator Agent

The role of the mediator agent is to address and com-

ment on some of the questions and requests that a user

with only a general knowledge of the tasks may have.

In this study, the mediator agent additionally performs

an action to increase either the personal resources or

job resources. The actions typically performed by the

mediator agent are as follows:

• Provide responses for the user by employing ﬁller

and nodding motions.

• Respond to the expert agent based on the prede-

ﬁned responses.

• Provide questions that are similar to the user pref-

erences to the expert agent, from the prepared list.

• Provide comments related to decision-making

factors that have not been considered by the user

and expert agent.

The mediator agent has knowledge of the major as-

pects of the decision-making. The questions and com-

ments by the mediator agent are focused on a few fac-

tors included in the proposals for decision-making.

When the mediator agent is conﬁgured to take ac-

tions that increase the user’s personal resources, it

performs the following actions additionally:

• Agree with the user’s opinions and requests.

• Concur on the aspects with which the user is pre-

occupied.

• Compliment the user’s decision making process.

• Nod elaborately after listening to the user’s opin-

ions and requests.

All these actions performed to increase personal re-

sources are independent of the proposal content. We

set the probability of action such that these actions do

not occur in succession and not appear contrived.

When the mediator agent is conﬁgured to perform

actions that increase the user’s job resources, it per-

forms the following actions additionally:

• Provide meta-cognitive suggestions regarding the

user opinions and requests, such as “There are

other forms of thinking.”

• Provide comments regarding aspects that are dis-

regarded by the user.

• Advise the user on decision making.

• Display thoughtful gestures after listening to the

user’s opinions and requests.

Actions that increase job resources are determined by

the emphasizing factors estimated from the user be-

havior. The advisory-like behaviors occur when the

emphasis factor can be estimated with a certain level

of conﬁdence.

2.2 Outline of the Interactive

Decision-making with the Agents

Figure 1 shows the components of the agents and the

outline of the data ﬂow. Both the mediator agent and

the expert agent estimate the user preferences, and

provide proposals and behaviors using the dynamic

estimation of emphasizing points (DEEP) proposed

in previous studies. DEEP is a method for estimating

the user’s emphasis factors based on verbal reactions,

body movements, and physiological indices (Ohmoto

et al., 2014). The physiological indices used in this

study are skin conductance response (SCR) and heart

Improving Active Attitude for Interactive Decision-making with Multiple Agents by Increasing Personal Resource

331

User

A mediator agent An expert agent

Estimation of

emphasis points

Estimation of

emphasis points

Nonverbal

Behavior

Decision

Utterance

Decision

Nonverbal

Behavior

Decision

Utterance

Decision

Proposal

Decision

Typical reactions

for the interaction

Typical reactions

for the interaction

Explanations of the

proposal

Actions for increasing

the job/personal

resources

physiological indices

utterance and

nonverbal behavior

utterance and

nonverbal behavior

Figure 1: The components of the agents and the outline of

the data ﬂow.

rate (this is converted to cardiac sympathetic index

(CSI) and cardiac vagal index (CVI)). The SCR be-

comes unresponsive when a person concentrates on

one object, and often becomes responsive when a per-

son focuses on various objects. The CSI reﬂects the

state of the sympathetic nervous system, and partic-

ipants with a high CSI are relatively tensed and ex-

cited. Furthermore, the CVI reﬂects the state of the

parasympathetic nervous system, suggesting that par-

ticipants with a higher CVI tend to be less tensed and

more relaxed. Using these physiological indices as

clue, DEEP estimates the user’s emphasis factors in

addition to the verbal and nonverbal information.

The proposals by the expert agent are categorized

into approximately ten categories, based on the major

factors for decision-making included in the propos-

als. The user repeatedly asks questions regarding the

proposal, and continues to evaluate the proposals pro-

vided by the agent until satisfaction is attained. The

expert agent’s potential proposal contents are selected

using a pre-prepared table. A few proposals that are

close to the combination of factors are prepared for

when the user requests a variation of the proposal with

similar factors. The answers to the user’s questions

are prepared in advance based on preliminary interac-

tions. If an appropriate answer is not included in the

list, the expert agent apologizes, and asks whether the

user has another question.

In addition, when some proposal candidates ap-

pear, the selecting method is different between for in-

creasing the personal resource and for increasing the

job resource. When the agent tries to increase the per-

sonal resource, the proposal is made to conform to the

user’s most recent preference. When the agent tries to

increase the job resource, the proposal is tailored to

give the user a relatively wide viewpoint.

We used Unity3D (http://www.unity.com/) as the

interface for the interaction agents. The agent could

automatically realize multimodal interaction behav-

iors, such as gaze, body motions, and speech. The

data related to verbal meanings were manually pro-

vided to the agents, such as whether a user response

was positive or negative and what question was asked

by the user, as these could not be efﬁciently deter-

mined in real time. Although the data related to ver-

bal meanings were manually provided, the agent au-

tomatically generated the output of verbal and non-

verbal behaviors that had been previously designed,

except for the answers to unexpected questions. We

refer to the agent control method using partially man-

ual inputs as the Wizard of Oz (WoZ).

The WoZ operator uses two commands: decision

of speech contents and change in proposal presenta-

tion. When the user makes an utterance and expects

a meaningful response from the agents, the operator

determines which agent should respond, and selects a

corresponding response from a prepared response list.

Subsequently, a command is returned to the agent.

The agent who receives the command provides the

selected response to the user. When the user requests

to change the proposal presented, the operator sends

the corresponding command to change the proposal.

The agent automatically provides some prepared re-

sponses for changing the proposal, and then changes

the proposal.

3 EXPERIMENT

The purpose of this experiment is to investigate the

effect of an agent behavior, which increased personal

resources for improving the active attitude of the par-

ticipants. In the experiment, the participants were

asked to plan a two-day trip to a ﬁctitious city with

two agents (an expert agent and a mediator agent).

The participants were randomly divided into three

groups based on the behavior of the mediator agent.

In the personal resource group (PR-group), the me-

diator agent’s function was to increase personal re-

sources. In the job resource group (JR-group), the

mediator agent’s function was to increase the job re-

sources. No mediator agent was involved in the no-

agent group (NA-group). The behavior of the expert

agent toward the participants was the same across the

groups. By comparing the behavioral data of these

groups, we investigate the effects of the agent’s inter-

actions on the participants’ active attitude.

3.1 Task

Planning the two-day trip involved the following: de-

termining three tourist destinations to visit, and the

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

332

Agent control PC

WoZ

environment

Display the agents’ behavior

Experimenter

Participant

Commands

Figure 2: The experimental setting.

route and order in which to visit them; arranging ac-

commodations and the length of stay at accommoda-

tion. Each destination was divided into six categories

depending on the factors that contributed to one’s en-

joyment, such as “sweets” and “sightseeing.”

The travel planning tasks were divided into three

phases: introduction phase, overview phase, and de-

tail phase. In the introduction phase, the participant

conversed with the agents. The conversation included

a self-introduction and explanation on how to proceed

with the task. In the overview phase, the participant

considered various sightseeing spots, tourist destina-

tions, and inns to determine the three destinations that

the participant wanted to visit. The overview phase

ended when the participant was satisﬁed. In the detail

phase, the route to travel was ﬁrst determined by con-

sidering the location of the tourist destinations and the

accommodation decided in the overview phase. Sub-

sequently, the participant could customize the details

of his/her travel plan based on the route. The detail

phase ended when the participant was satisﬁed with

the customization.

In this task, the mediator agent’s behavior was dif-

ferent for each group. In the PR-group, the content of

the destination proposed by the mediator agent em-

phasized the nearest preference of the participant. In

the JR-group, the mediator agent increased the job re-

sources for the participant. The content of the destina-

tion proposed by the mediator agent included factors

that were relevant to the preferences of the participant

but had not been considered so far. In the NA-group,

no mediator agent was involved (a participant and an

expert agent collaborated on the task).

3.2 Experimental Setting

The experimental setting is shown in Figure 2. The

participant was seated in front of a 60-inch monitor

displaying the agents and the map of the travel des-

tinations. The participant’s voice was recorded us-

ing microphones, and the participant’s behavior was

recorded using two video cameras. The participants

interacted with the agents using only his/her voice. To

estimate the mental state of the participant, the SCR

and heart rate were recorded using a device (Polymate

mini). These were sent to the agents in real time.

After a brief explanation of the experiment, elec-

trodes for measuring the SCR and heart rate were at-

tached to the participant’s left hand and chest. Af-

ter a 2-min relaxation period, the experimenter turned

on the video cameras and commenced recording of

the physiological indices. The participants performed

the three phases of human-agent interactions sequen-

tially. The total time of the experiment was approxi-

mately 1 h.

The participants in the experiments were 45

Japanese undergraduate college students (27 men and

18 women). The average age was 23.2 years (standard

deviation 3.28). The participants whose physiological

indices could not be accurately obtained were elimi-

nated. The PR-, JR-, and NA-groups included 13, 15,

and 14 participants, respectively.

3.3 Result

3.3.1 Reaction Latency of Utterances

To induce a user/person’s active attitude towards the

interaction with agents, we focused on an interaction

cue which is related to the mental states of people ob-

servable in conversations. Bechade et al. explored

behavioral spoken cues in human-robot interaction

(Bechade et al., 2015). In the study of Bechade et

al., they found a signiﬁcant interaction between self-

conﬁdence and the number of speech segments, self-

conﬁdence and the speech reaction time, and enthu-

siasm and the participant speech duration. Ono et al.

focused on synchronous behaviors of two robots and

a person close to two robots became easily involved in

their communication (Ono et al., 2016). In the study

of Ono et al., they focused on overlapping interaction.

The study also suggested that participants felt that the

degree of communication activity became lively by

presenting the overlapping interaction.

From the cues considered in these studies, we

analysed reaction latency of utterances for conﬁrming

the active attitudes of the participant. We expected

that a participant of interaction with an agent which

showed the short reaction latency would feel active

engagement of the agent towards the conversation.

The reaction latency from the end of the agent’s

utterance to the start of the participant’s utterance was

measured. The ﬁller was not regarded as an utter-

ance. Figure 3 shows the average of the latency in

the task. We performed a 2 (phase: overview or de-

tail) x 3 (group: PR, JR, or NA) analysis of variance

Improving Active Attitude for Interactive Decision-making with Multiple Agents by Increasing Personal Resource

333

overview detail

PR JR NA

The latency of

utterances

in the task

Figure 3: The latency of utterances in the task.

overview detail

PR JR NA

The

number

of changes

emphasizing

factors

Figure 4: The number of changes of emphasizing factors.

(ANOVA) on the data of the reaction latency of the

participants’ utterance. Consequently, signiﬁcant dif-

ferences were observed among the phases and groups

(phase: F(1, 39) = 36.39, p < 0.0001; group: F(2, 39)

= 4.99, p = 0.012).

We performed multiple comparisons among the

groups using Ryan’s method. Signiﬁcant differences

were shown between the PR- and JR-groups (PR <

JR, p = 0.0050), and between the PR- and NA-groups

(PR < NA, p = 0.023). Although the reaction la-

tency became longer in the detail phase across all

the groups, the PR-group remained at the level of the

overview phase in the other groups. Therefore, we

infer that by increasing the personal resources, the

reaction latency of the participant can remain short,

without being affected by the decision-making con-

tent. The results suggest that we were able to in-

duce active attitudes towards the interaction with the

agents to some extent. In addition, it is important to

predict the behavior of others for remaining the reac-

tion latency of utterance short. We perhaps infer that

increasing the participant’s personal resources facili-

tates the estimation of the agent’s behavioral model.

3.3.2 Emphasizing Factors in the

Decision-making

In this task, DEEP was used to perform an interac-

tion by dynamically estimating the participant’s em-

phasis factors. To investigate the effect of interacting

with the agents on the decision-making, we counted

the number of times that a participant’s prioritization

of the emphasizing factors changed during the task.

Figure 4 shows the average of the number of changes.

We performed a 2 (phase: overview or detail) x 3

(group: PR, JR, or NA) ANOVA on the data of the

overview detail

PR JR NA

The

number of

the detailed considerations

Figure 5: The number of the detailed considerations.

number of times that a participant’s prioritization of

the emphasis factors changed. Consequently, a sig-

niﬁcant difference in phase was observed (F (1, 39) =

8.83; p = 0.0051). The paired t-tests in each group be-

tween the overview and detail phases revealed signif-

icant reductions in the JR- and NA-groups (JR: t (14)

= 3.09, p = 0.008; NA: t (13) = 2.7; p = 0.018) but not

in the PR-group (t (12) = 0; p = 1.0). This implies

that the participants in the JR- and NA-groups did

not change their preferences in the detail phase. The

participants in the PR-group did not adhere to their

own preferences even in the detail phase; they contin-

uously considered their emphasis factors throughout

the interaction with the agents. This suggests that the

increased personal resources of the participants en-

sured that they maintained their active attitude toward

improving their travel plans in the detail phase.

To conﬁrm this, we counted the number of times

that the participants considered parts that were not

mentioned by the agent. Figure 5 shows the average

number of the detailed considerations. We performed

a 2 (phase: overview or detail) x 3 (group: PR, JR, or

NA) ANOVA on the data of the number of times of the

detailed considerations. Consequently, a signiﬁcant

difference among the phases was observed (F(1, 39)

= 7.28; p = 0.0010). The paired t-tests in each group

between the overview and detail phases revealed no

signiﬁcant increases in the speciﬁed values in the JR-

and NA-groups (JR: t (14) = -1.13, p = 0.28; NA: t(13)

= -1.20, p = 0.25) but a signiﬁcant increase in the PR-

group (t(12) = -2.23, p = 0.046). Therefore, we in-

ferred that increasing the personal resources induced

the active attitude of the participants toward the task.

3.3.3 Physiological Indices

To investigate whether a change that was not appar-

ent from the behavior occurred in the human men-

tal state, we analyzed the heart rate indices (CSI and

CVI). CSI is one of the indices of sympathetic nerve

activity. The sympathetic nervous system’s primary

function is to stimulate the body’s ﬁght-or-ﬂight re-

sponse, such as tension and excitement. The CVI is

one of the indices of parasympathetic nerve activity.

The parasympathetic system is responsible for stimu-

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

334

1.5

1.6

1.7

1.8

1.9

overview detail

PR JR NA

The

average

of CSI values

Figure 6: The number of times of reversed between SCR to

CSI and CSI to SCR.

The

average

of CVI values

4.5

5.5

6.5

overview detail

PR JR NA

Figure 7: The number of times of reversed between SCR to

CVI and CVI to SCR.

lating the “rest-and-digest” activities that occur when

the body is at rest and relaxed.

We calculated the CSI values in each phase

(overview or detail) for each group. The results are

shown in Fig. 6. We performed a 2 (phase: overview

or detail) x 3 (group: PR, JR, or NA) ANOVA on the

data of the CSI values. Consequently, a signiﬁcant

difference was observed across the phases (F (1, 39)

= 5.42, p = 0.025). The paired t-tests results in each

group in both the overview and detail phases show no

signiﬁcant increases in the PR- and NA-groups (PR: t

(12) = -0.52; p = 0.61; NA: t(13) = -1.55; p = 0.14);

however, a signiﬁcant increase was observed in the

JR-group (t (13) = -2.33; p = 0.035). These results

suggest that the tension and excitement of the partici-

pants increased in the detail phase in the JR-group.

We also calculated CVI values in each phase

(overview or detail) in each group. The results are

shown in Fig. 7. We performed a 2 (phase: overview

or detail) x 3 (group: PR, JR, or NA) ANOVA on the

data of the CVI values. There was no signiﬁcant dif-

ference across the groups and phases. This implies

that the actions of the mediator agent directed toward

increasing the job or personal resources did not affect

the participants mentally in both the rest and relaxed

states.

4 DISCUSSION

In the experiment, the mediator agent increased the

personal resources and job resources in the PR- and

JR-groups, respectively. The expert agent increased

the job resources in any group. According to Xan-

thopoulou et al. (Xanthopoulou et al., 2009), the two

resources are interrelated, and when both resources

are fully acquired, they inﬂuence each other, and in-

duce higher work engagement. Therefore, in this

study, it is assumed that both the expert agent that in-

creased the job resources and the mediator agent that

increased the personal resources are important factors

toward increasing a participant’s active attitude in in-

teracting with the agents. As the results of the analy-

sis, it is considered that, by the agent which increases

the personal resource, the active attitude of the par-

ticipants was induced to some extent both for the in-

teraction with the agents and for the execution of the

task.

However, no difference was found in the active at-

titude of the participant based on whether there is a

mediator agent increasing the job resources. This im-

plies that as the job resource is increased to a certain

limit, and further actions did not signiﬁcantly inﬂu-

ence the participant’s attitude. On the other hand,

there was a signiﬁcant difference in the value of the

physiological index (CSI), and the mediating agent

that increased the job resource tended to enhance

a participant’s stress. Considering the participants’

stress levels, the effect of the support by the agents

is not always positive.

We performed analysis to identify the changes

in the human participants’ inner states using physi-

ological indices (CSI and CVI in the result section).

However, there were diverse stimuli in the experi-

ment, and the human inner state varies were contin-

uously affected by these stimuli. To eliminate these

effects, we focused on the causal relationship between

the changes in several physiological indices. The

“CausalImpact” package of R 3.6.0 was used to es-

timate the causality. The analysis in the result sec-

tion shows that the CSI is affected by the agent’s ac-

tions directed toward increasing the job or personal

resource. Although the result was not demonstrated,

no signiﬁcant difference was observed in the SCR

measured in addition to the CSI. We thus examined

the effect of the agent inﬂuence on the change in

the CSI using the SCR value as a covariate. The

CausalImpact package was used to calculate the value

of the point-wise impact in the 30-s data before and

after the agent’s action directed toward increasing the

job or personal resource. A t-test was performed to

determine whether the values changed following the

agent’s action differed between the PR-group and JR-

group. There was a signiﬁcant difference (t (161) =

-2.05, p = 0.042; JR > PR). From the analysis it can

be deduced that the intensity of the effect of the agent

actions on the CSI can be observed, taking into ac-

count the change in the participant’s mental state, as

Improving Active Attitude for Interactive Decision-making with Multiple Agents by Increasing Personal Resource

335

reﬂected in the SCR. By estimating the causal rela-

tionships between the variables that serve as clues to

inferring the changes in a person’s mental state, we

may be able to compare the inferred changes while

eliminating various convoluted factors in future work.

5 CONCLUSION

The aim of this study was to investigate whether an

active attitude can be induced in a person by apply-

ing the framework of work engagement enhancement

to a cooperative decision-making task with agents.

We conducted experiments to evaluate the effect of

an agent behavior directed toward increasing personal

resources for improving the active attitude of the par-

ticipants. In the experiment, the participants were

asked to plan a two-day trip to a ﬁctitious city with

two agents (an expert agent and a mediator agent).

There were three groups; PR-group: the mediator

agent serves to increase personal resources, JR-group:

the mediator agent serves to increase job resources,

and NA-group: no mediator agent is involved. From

the results, we suggest that the mediator agent can in-

duce and maintain the participant’s active attitude to-

ward the task and the agents by encouraging the par-

ticipant to increase his/her personal resources. In fu-

ture work, we will consider a method for analyzing

the detailed inner state changes of the participants in-

volved in the task.

ACKNOWLEDGEMENTS

This research is supported by Grant-in-Aid for Young

Scientists (B) (KAKENHI No. 16K21113), and

Grant-in-Aid for Scientiﬁc Research on Innovative

Areas (KAKENHI No. 26118002) from the Ministry

of Education, Culture, Sports, Science and Technol-

ogy of Japan.

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