What Creates a Pleasant Mood?
－ Development of an Information Model to Create a Good Mood －Masahide YUASA†

Hidefumi OHMURA††,‡ andDaisuke KATAGAMI‡†Shonan Institute of Technology, 1-1-25 Tsujido-Nishikaigan, Fujisawa, Kanagawa, 251-8511, Japan††Department of Adult Mental Health, National Institute of Mental Health,National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo 187-8553, Japan‡Tokyo Polytechnic University, 1583 Iiyama, Atsugi, Kanagawa, 243-0297, Japan
E-mail: †yuasa@sc.shonan-it.ac.jp
Abstract Several previous studies have investigated verbal and nonverbal information that may induce a good or badmood by observing human behaviors; however, to the best of our knowledge, a model that can influence the mood of arobot/agent has not been developed thus far. In this study, we develop an information model for creating an environment that isconducive to pleasant interactions between humans and robots/agents. We propose an information model for mood estimation
based on the innate tendency of curiosity. By using the proposed information model, we can estimate the verbal and nonverbal
behaviors of robots/agents that are suitable for inducing positive or negative moods according to a given situation.
Keywords Animated agent，robot，mood, mood engineering, information model, human agent interaction
However, a robot/agent model that can estimate the1. Introduction
In our everyday conversation , we comprehend others’

next mood has not been developed thus far, even thoughemotions by observing nonverbal cues, to understandseveral previous studies have investigated verbal andwhat they are thinking and ensure smooth communication.

nonverbal information. An information model that canWe do not just comprehend individuals’ emotions but also

estimate the next mood across multiple situations isshare in conversational moods created by multiple people

desired to develop robots/agents that can create a pleasant(i.e., an enjoyable or excited mood at a party, bored moodmood. Moreover, the model should have simplified

during a repetitive speech, or grief at a funeral).

principles to enable wide applicability across multiple

Sharing moods is crucial because it contributes tosituations [1]maintaining and improving everyday life activities; once

Therefore, we propose an information model for mood,

we understand the thoughts and feelings of a group, webased on the innate curious tendency of humans. Researchtend to unify and share them, in order to sustain the moodon curiosity illustrates the principle that people likeof the group. The mood of a group can also be changed tocomplex

improve a situation, based on the actions of one person.

emotions [2]. We hypothesize that this principle isUnderstanding and improving mood can benefit groupapplicable to various kind of human behaviors; thus, theactivities and every life (by enabling cooperative work,complexity of mood information is also related tothe creation of new ideas, and good decision-making,people’setc.)

may

expresses the relationship between the complexity ofcontribute to improvement of human communication, andinformation and emotions , we can estimate the behaviors

may produce new communication methods for humans.

of robots/agents that are suitable for inducing moods

Therefore,

investigations

regardingmood

Furthermore, we believe that the study of mood caninformation, and thatemotions.

Using

theit

influences humanproposedmodelthat

according to a given situation.

inform the study of interactive robots/agents that are

In the following section, we explain Berlyne’s model

capable of communicating with people. Much research[2], which expresses the relationship between information

has been aimed at developing interactive conversationaland emotion. Based on Berlyne’s model, we propose anrobots/agents, which can smoothly communicate withinformation model that expresses the relationship between

people. If robots/agents are developed that can also create

emotionsa particular mood in a situation, this will enable major

Additionally, we demonstrate examples of applications

advancesusing our model.

inrobots/agents.

relationships

betweenpeopleandandsuitablebehaviors

ofrobots/agents.

Furthermore, we propose a new area of study in terms

of the creation of mood by multiple robot s/agents, which

information and emotion.

we refer to as “ mood engineering” and investigate theWe hypothesized that this principle could be applied tocomprehension and creation of moods in conversations

other areas. In music research, there has been a focus on

between robots/agents and humans.

expectations

ofinformationwithconsiderationofBerlyne’s model. Meyer described that deviations of2. Related Research

expectations increase emotions when we listen to music

According to the model of multiple party communication[8], and Narmour also focused on the relationshipand emotion, Barsade et al. investigated group emotion

between expectation and deviation [9]. Ohmura et al.

[3], and Bartel et al. categorized emotions observed inproposed a new model that could generate a melodic

conversations, and proposed a circumflex model, in whichrhythm based on entropy [10]. Ohmura also insisted thatmoods are represented in two-dimensional space, in terms

the model might be related to creating mood [11]. Basedof valence and arousal [4]. However, these researchers

on their study, we propose an information model for

have not proposed a mood generation model that cancreating mood.

estimate the next mood.
Severalresearchershavedeveloped

multiple party

3. Information Model for Creating Moodinteractive robots/agents. Padilha et al. [5] developed a

We focus on a model of complexity in order to develop

simulation system, which mimics small group discussion.

a model of robot/agent behaviors. As described, weYuasa et al. [6] proposed a turn-taking agent system based

propose an information model to create mood based on

on a social scientific approach. The aim of these studies

Berlyne’s proposal. We hypothesize that good/bad moods

was to develop a conversational agent who could start

are related to information ind icated by various factors inspeaking when the first speaker agent finished speaking;verbal and nonverbal communication. In particular, wethey did not focus on mood generation or the informative

expect that entropy values may relate to various factors

model. A model that expresses the relationship between

such as turn-taking patterns, complexity of opinions inconversation information and emotions (pleasant) is

discussion,desired for effective conversational robots/agents that canparticipants in group work. Moreover, our model follows

create a pleasant mood.

Berlyne’s

Researchers in the areas of psychology and brain

information in conversation increases, good mood issciences have investigated the study of human curiosity,generated. However, when complexity/entropy is too high,and the relationship between complexity of information

mood becomes negative. This relationship is illustrate d by

and emotion. Hatano et al. argued that there are several

an inverted U-shaped graph, which is the same as

types of curiosity, one being the need to obtain more

Berlyne’sknowledge and information [7] and being pleased withrelationship between complexity/entropy of information

receiving novel information. This type of curiosity isand good/bad mood, and the verbal and nonverbal

considered to be caused by a “hunger for information,”behaviors of robots/agents that are suitable for indu cingwhichpositive or negative moods.

isaninnate

needinhumans

thatinduces

exploratory behavior. In other words, people seek daily

andmodel;model.

varietyofwhen

TheIn the next section,

thefacial

expressions

complexity/entropy

proposedmodel

showsofof

thewe show two examples ofnews and love to gossip. Curiosity may play an important

applications of our concept: a conversation system usingrole in activating human activities.

multiple agents that presents different opinions, and a

Berlyne proposed the optimal-complexity model [2],comment support system for a video hosting service using

which indicates the relationship between pleasure andmultiple characters.

complexity of information expressed by entropy value;
for example, when complexity increases and entropy

4. Method and Results

values rise, people feel good. However, when complexity

4.1 Proposal of a conversation system using multiple

is too high and entropy values rise too much, people doagents that presents different opinions

not feel good. For example, in daily life; once a rumor isWhen someone agrees with our opinions, it pleases us.

repeated, people become tired of it and eventually are not

The more people follow our opinion, the more we areinterested at all and feel disgust. Thus, the Berlyne study

pleased. On the other hand, in addition to increases inis excellent at formalizing the relationship between

those who agree with us, increases in those who do notagree with us may activate an excited mood in situations

input. When a user is inputting a comment, the next moodin which various opinions are desired, such as discussion

is predicted by the comment, and facial expressions ofand brainstorming. Thus, too many of the same opinion s

characters change (Fig. 2). The extent of changing facial(too many people agreeing with just one opinion) creates

expressions reflect the appropriateness of comments. Fora bored mood, and different opinions may create a better

instance, if the comment is very inappropriate, allmood. We believe that whether the same or different

characters’ faces change to sad ones. It is expected that

opinions are expressed affects complexity/entropy, andthese sad faces may deter the user from inputting theirthat the relationship between complexity/entropy andcomment. If the comment is not overly hurtful but also

excited/bored mood can be explained in this way.

not positive, some faces (not all faces) change. Moreover,

Based on this concept, we developed a test system

which characters’ faces change is decided by the system,

using two animated agents that discuss different opinions

using entropy values. We hypothesize that random faces(Fig. 1). In this system, both agents agree with a user ’s

are more effective for inducing users’ hesitation thanopinion, or one agent agrees while the other does notarranged faces (Fig. 3). Complexity/entropy may beagree. In our preliminary test using the system, users felt

useful to estimate which pattern of faces is effectiv e tobetter

elicit user hesitation.

whenbothagents

agreed,contrarytoourexpectations. Based on these results, the estimation
method using the information model can be improved, andthe number of agents should be adjusted. There issignificant value in defining the kinds of opinions related
to complexity/entropy and mood. In the future, we aim toconduct additional tests to investigate the relationshipbetween opinions of complexity/entrop y and mood inconversations.

Fig. 2 Comment support system for a video hostingservice.

Whenauserinputs

acomment,

theexpressions of displayed faces change.

Fig. 1 Multiple agents that discuss different opinions

4.2 Proposal of a comment support system for a video
hosting service using multiple charactersVideohostingsites,

forwhichusers

Fig. 3 Arranged faces (upper) and random faces
caninput(lower)comments for each video, have been developed and areenjoyedby

many users. However, there are many

5. Discussion and Conclusions

inappropriate and hurtful comments shared, because

We proposed an information model for creating ananyone can comment anonymously. It is desirable thatenvironment that was conducive to pleasant interactions

users do not share hurtful and mean comments; however,between humans and robots/agents. This model could bethere is not yet an effective method.

crucial to the development of an environment in which

Therefore, we propose a new method using multiple

humans can enjoy their interact ions with conversational

characters; each character has facial expressions thatrobots/agents. We focused on the study of curiosity as anexpress the mood for the comment that a user wants toinnate tendency of humans, to make an information model

for mood estimation. Using the proposed information[5] Padilha, E.G. and Carletta, J. A simulation of smallmodel,group discussion. In Proc. EDILOG 2002, 117 –124,

webehaviors

canofestimate therobots/agents.

verbalWeandnonverbal

demonstrated

twoexamples of applications that applied to our concept.
As we described, the model is applicable to many areas,(2002).
[6]Yuasa,M.,

AutonomousTokunaga,

Turn-Taking

H.,

AgentandMukawa,SystembasedN.
onand we expect that the model can explain the mechanism

Behavior Model. In: Proc. HCI International 2009, pp.

of turn-taking patterns and generation of mood. Yuasa et368–373 (2009).

al. proposed a turn-taking simulation system based on[7] Hatano, G., and Inagaki, K. Intellectual curiosity.

observations of human conversation (Fig. 4) [12 -14], andChuko-Shinsho, (1973). (in Japanese)

investigated the relationship between turn-taking patterns[8] Meyer, L.B. Emotion and meaning in music. Chicago:and emotion; for example, moods can be represented inUniversity of Chicago Press, (1956).

two-dimensional space, in terms of valence and arousal.

[9] Narmour, E. The analysis and cognition of basic

However, there is no suitable model to explain themelodic structures. Chicago: University of Chicago Press,

mechanism of these moods. Complexity/entropy may be a(1990).

significant factor in the explanation of this mechanism. In[10] Ohmura, H., Shibayama, T., Shibuya, T., Takahashi,

the future, we will define complexity/entropy and testT., Okanoya, K., and Furukawa, K. Modeling of Melodic

mood estimation for the turn-taking system.

Rhythm Based on Entropy toward Creating ExpectationWe

believethattherelationshipbetweenand Emotion. In: Proc. SMC 2013, pp. 69–73 (2013).

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Annual Conference of the Japanese Society for Artificial
Intelligence, (2014). (in Japanese)[12] Yuasa M., Mukawa N., Kimura K., Tokunaga H., andTerai H. An Utterance Attitude Model in Human-Agent
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