Comparative Study of Human Behavior in Card Playing ... - Springer Link

Int J Soc Robot (2014) 6:5–15 DOI 10.1007/s12369-013-0184-0

Comparative Study of Human Behavior in Card Playing with a Humanoid Playmate Min-Gyu Kim · Kenji Suzuki

Accepted: 27 February 2013 / Published online: 15 March 2013 © Springer Science+Business Media Dordrecht 2013

Abstract This paper describes the study of human behaviors in a poker game with the game playing humanoid robot. Betting decision and nonverbal behaviors of human players were analyzed between human–human and the human– humanoid poker game. It was found that card hand strength is related to the betting strategy and nonverbal interaction. Moreover, engagement in the poker game with the humanoid was assessed through questionnaire and by measuring the nonverbal behaviors between playtime and breaktime. The findings of this study contribute to not only design of socially interactive game playing robot, but also the theoretical approach on the realization of the robot that behaves in the way of human doing in game playing. Keywords Human–robot interaction · Social playmate · Poker game · Humanoid

1 Introduction Current advanced technologies offer many chances in a number of different ways to play games with each other. In modern life, people enjoy games through video game and social network game and now even play games with robots. The people factor of game that creates opportunities of social experience such as competition, teamwork, M.-G. Kim () Department of Intelligent Interaction Technologies, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki, Japan e-mail: [email protected] K. Suzuki Faculty of Engineering, Information and Systems, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki, Japan e-mail: [email protected]

social bonding and personal recognition [1] plays an important role in either developing game playing robots as a social partner in the domain of human–robot interaction. Many researchers have made efforts to exploit game playing robots. Neil et al. have successfully implemented physical chess playing robot capable of the motion grammar for the manipulation of chess pieces [2]. Frank et al. introduced that an industrial robot arm system with vision camera plays German board game, Mensch ärgere dich nicht while projecting board game field onto the workbench [3]. On the other hand, there are game playing robots with communicative abilities. A poker playing agent that exhibits emotions to communicate with human player through multimodal channels [4]. A virtual character plays chess by physically manipulating chessman while speaking and expressing several emotions [5]. These game playing robots have been designed from robot-centric interaction point of view. In this study, we focus on poker game that is the optimal social setting to observe human complex behaviors and social interactions. During the game, player usually needs to gain advantage with hiding intention. They attempt to play carefully because they need to see the game pertaining to a financial exchange. Besides, people engage in social communication and interaction while deciphering other’s intention from verbal and nonverbal behaviors or sometimes behaving deceptively for strategic purpose. Furthermore, people can make new social contact and build social relationship with fellow players through the poker game. In such a social, friendly game environment, a poker playing humanoid robot to understand human interactions and socially respond to human in the game is essential as well as establishing computational model of game strategies and opponent decisions.

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In order to direct the goals, the following studies should be investigated; (1) what kind of human behaviors the robot has to perceive, and (2) how the robot creates social response on the basis of perceived human behaviors. The first study examined several comparative effects between human–human poker game and human–humanoid poker game. Considering human–centric interaction design of poker playing robot, it is important to study the human behaviors in human–robot poker game as compared with human–human poker game. Many researchers in humanities and social sciences have presented their studies about the human behaviors related to poker game setting for a long time [6–10]. Applying the discoveries of the human studies to designing socially interactive poker playing robot is highly questionable because it is unsure whether the same human behaviors would be observed in the human–robot poker game. In order to clarify this kind of vague of whether human–human communication can be directly transferred into human–robot interaction, Walters et al. compared the human–robot personal spatial zones with the aspect of human–human personal spatial zones [11]. Mutlu et al. attempted to explain the task structure and user attributes of human–robot interaction design through comparative study of human–human interaction [20]. The purpose of the follow-up study is to analyze relationship between human current situation (player’s own hand strength) and behaviors (his nonverbal behavior and decision-making) in human–humanoid poker game. As usual in human–human poker game, it is seen that player’s psychological state is affected by his own hand strength and shows up through face, body and decision in any way. Hence, a player who holds strong hand tends to try to disguise his hand as being weak for strategic purpose. If the same relationship in human–humanoid poker game is found also, it can be critical for robot to understand human behaviors, which means the robot predicts human fellow player’s own hand strength by observing his nonverbal behaviors and analyzing game strategic decisions. Such robots that understand and imitate human behaviors are distinguished from usual poker agents capable of computing winning strategies with efficient decision-making algorithm designed to cope with imperfect information [12]. The agents accurately evaluate the game rather than understand complex dynamics of human interactions. From the second study, we expect that the human-like characteristics of poker game robot successfully will encourage a human social behavior and keep people motivated to play the game. In Sect. 2, we establish theoretical frameworks by reviewing relevant works. Section 3 describes the methodology of this study in detail. We introduce our humanoid platform, experimental procedure and measured human behaviors. Sections 4 and 5 show the experimental results and general discussion.

Int J Soc Robot (2014) 6:5–15

2 Theoretical Background 2.1 Competitive Card Playing with Humanoid Robot Preceding studies have examined effects of robot on human behavior and perception based on cooperative type of interactions. For instance, it has reported how the robot’s physical presence influences a person’s perception in the cooperative task and the differences between a robot and an animated character in terms of engagement and perceptions [16, 17]. The effect of physical embodiment on the feeling of an agent’s social presence has been evaluated by letting the participants interacting with the agent like touching [15]. The result showed that lonely people provide more positive responses to the agents than non-lonely people. Kanda et al. have measured the body movements of human who interacts with the physical robot and found the positive correlations between cooperative body movements and subjective evaluations [18]. Moriguchi et al. have examined in the card sorting task with an android whether it triggers young children’s perseveration and compared the effects of the android with those of a human model [19]. Meanwhile, Mutlu et al. have researched on user attributes in competitive task as compared with cooperative tasks [20]. They have introduced significant differences of task structure such as that involvement in the human–robot interaction was significantly higher in the competitive task than in the cooperative task. The differences might have relevance to the findings that people in competitive group display more rejection to the other and believe other persons are uniformly competitive while people in cooperative group evaluate their group members more positively [13, 14]. In our study, it is assumed that people will see robot as competitor in competitive game task like people usually does in human–human competitive game. More specifically, it is expected that people will show different type of response toward human competitor and robot competitor because robotic appearance, mechanical gestures and speech synthesis would make people less absorbed in the competitive task itself and more interested in interacting with robot. Therefore, we hypothesized that in poker game with robot opponent, people will show different aspect of betting decision and nonverbal interaction as compared with poker game with human opponent. • Hypothesis 1: When participants play poker game with robot opponent, they will show different betting decision and nonverbal interaction as compared with poker game with human opponent. 2.2 Poker Game In the domain of artificial intelligence, poker game is an interesting test bed because it includes imperfect information,


competitive agents, risk management, agent modeling, deception and dealing with unreliable information [12]. In order to confront the professional human poker players, solving these difficulties is the main issue in developing poker agent. However, poker in real life has two not mutually exclusive reasons why the poker game we play cannot be a complete zero-sum game [21]. (a) “Some players make wrong decisions most of the time because their mental model of the mathematical structure of the game and the psychological nature of the opponents is false or incomplete”. (b) “Social reasons, enjoyment, and all sorts of personal considerations may contribute to the utility value of particular actions in the game”. Therefore, it is not easy for players to use a hypothetical normative strategy based on money. This study pays attention to the game playing humanoid as a social, friendly partner rather than gambling machine to deal with regular or professional players. If we obtain the mental model of human players as applied to the robot by combining the mathematical structure of the poker game with the psychological model of the opponent, it will be likely to perform as human does in poker game of real life. We first evaluate the bluffing strategy on the basis of money, and then look into whether card hand strength is associated with bluff decision and nonverbal interaction. • Hypothesis 2: Participants who play with the robot opponent will be affected in their decision and nonverbal interaction by their given hand strength as much as the participants who play with the human opponent is done. • Hypothesis 3: Given hand strength to participants is related to their betting decision and nonverbal interaction. 2.3 Engagement in Game with Humanoid Engagement is an important matter of successfully attracting people into a human–robot interaction. According to [22], when a sense is more immersed in the interaction, the experience is likely to be more compelling. Karolien et al. established a categorization of digital game experience in terms of in-game and post-game using focus group method [23]. Regarding suspense dimension, they categorized challenge, tension and pressure into in-game experience and release, relief and exhausted into post-game experiences. In a poker game, players also similarly go through mutually exclusive states in every turn such as in-game state (playtime) and post-game state (breaktime). When poker players experience in tension and release alternately, a pattern of their nonverbal interaction is changed. The use of eye gaze has been discussed to evaluate user’s engagement in a conversation task with virtual agent and robot [24, 25]. This study bases on the eye gaze to measure how people are engaged in the poker game with our robot.

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Fig. 1 Humanoid robot platform used in this study, named Genie

Abdullah et al. have already compared the frequency of occurrence for three events of eye gaze in a social game; ‘looking at opponent’, ‘looking at team member’ and ‘looking at game board’ to identify social interaction trends and game involvement [26]. • Hypothesis 4: Participants who play with the robot opponent will show a different interaction pattern of eye gaze between playtime and breaktime. • Hypothesis 5: Subjectively evaluated engagement will show a difference between human–human and human– humanoid poker game.

3 Overview of Experimental Setup 3.1 Humanoid Platform As can be seen from Fig. 1, the humanoid robot used in this study has been built to investigate human behavioral analysis [27]. The humanoid robot with the upper torso that includes 3DOF waist mounted on the wall has totally 32DOF (8DOF for the head, 3DOF for the waist, 7DOF for the right arm, 4DOF for the right hand, 5DOF for the left arm and 5DOF for the left hand). The humanoid platform has anthropomorphic size and shape. It does not have a face to exhibit emotions, but two eyes equipped with vision camera can imitate human gaze. 3.2 Participants Ten participants (aged 22–29, 6 males and 4 females) and a paid dealer were recruited from local communities in University of Tsukuba. The participants are undergraduate and graduate students with a wide academic background in education, physics, nursing, computer science, etc., and computer-friendly as mostly use a computer 8 or more times a week. They are non-professional, beginner-leveled players who barely play a poker game such as playing less than once a month. Some of them have never played poker game

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3.4 Simplified Rule for Two-Person Texas Hold’em

Fig. 2 Experimental setup: participants played Texas hold’em both with (a) human and (b) humanoid

before. The participants has never seen and also interacted with real robots. Since non-experience of the participants in both poker and robot prohibits from the biased response, the experimental results would fairly confirm the effect of opponent presence, the effect of card hand strength and engagement. The dealer is a graduate student who has many experiences in psychological studies. She was trained in advance to master the rule of Texas hold’em and the purpose of the experiment. Also, the dealer was carefully instructed not to talk and smile a lot, in order to minimize the effect of dealer’s speech and behaviors on the participant’s decision and interaction. The dealer spoke only to notify the betting turn to the players. 3.3 Procedure Each participant played Texas hold’em first with the human experimenter and then with the humanoid one on one as shown in Fig. 2. The dealer explained how to play the simplified Texas hold’em to the participants before the experiment and then the participants practiced the game until they got a hold of the game. The participants played five rounds of the poker game with play bills. The entire episode was recorded. The video cameras in a human–humanoid poker game were installed in the humanoid’s body and right side of the humanoid whereas a video camera was set in right side of the experimenter in the human–human poker game. The experimenter in the human–humanoid poker game operated the humanoid’s whole motions and made speech synthesis behind the experimental space according to Wizard of Oz approach [28]. The humanoid was manually controlled to display informative behaviors such as affirmative head nods, negative head shakes and head orientation (i.e., gazing at human player, looking at table and looking at a card on the hand) as well as to play Texas hold’em with its arm and hand. For time-saving, the dealer assisted the humanoid to easily handle the cards. The play bill was also handled by the dealer. Instead, the humanoid raised the stakes or folded by saying “bet one thousand yen” or “fold”.

The original rule of Texas hold’em was modified to simplify the betting structure. Each betting round, the participants can bet from a thousand yen to three thousand yen. As usual, in the game, the players share five community cards that are opened on the table and have two own cards. To win the game, the players should find the best combination among five community cards and two own cards during the game. The players included a blind bet before the card distribution. After the blind bet, the players place their bet with the first three community cards (flop cards) and their cards (pocket cards). When the tokens which a player bet would be equally matched by his opponent, the first betting round ends and the fourth community card (turn card) is opened. Then the players begin to bet money again. The round closes when the bet is matched equally by the opponent. The final community card (river card) is opened and final bet is placed. After the end of the final betting round, the players open their pocket cards for a showdown. If one of the players folds in the middle of the game, the game is over and the pocket cards are not revealed. Each participant played the poker game five times with the experimenter and the humanoid respectively. 3.5 Robot Movements The robot movements in the experiment are categorized into two different motions as shown in Fig. 3, manipulative and expressive motions. The manipulative motion is to pick and lift up its own pocket card to check the card suits. The humanoid performs the ‘Checking own Card Suits’ action using its left arm and rotating head only when the pocket card is distributed to the players in the beginning of the game. The expressive head movements create a sense of looking at table or its opponent. It executes the ‘Looking at Table’ action when the participants bet. ‘Looking at its Opponent’ action is done when the robot bets. The profile of robot motion and speech used in the experiment is described in Table 1. 3.6 Manipulation of Card Hand Strength Poker player’s nonverbal behaviors are related to his own card hand [29]. Since the participants were not professional, it was assumed that they would show appreciably different responses according to the card hand strength. The card hand strength was established as a primary independent variable in this experiment, and the poker game of five rounds was manipulated prior to the experiment to observe an effect of card hand strength on human nonverbal response. Given card hands were divided into two groups; strong hand and weak hand as shown in Table 2. The participants played Texas hold’em with the strong card hands


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Fig. 3 Snapshots in the demonstration of Genie’s Texas hold’em playing: (a) is the manipulative arm and head movements to display ‘Checking own Card Suits’ and (b) is the expressive head movements to create a sense of ‘Looking at Table’ and ‘Looking at its Opponent’ Table 1 Robot action and speech profiles according to game sequence during the experiment Betting structure

Game sequence description

Blind bet

Flop (first)

Turn (second)

River (final)

Robot action

Robot speech

Betting without any cards

Looking at opponent (expressive)

Saying ‘Bet XXXX yen’

Dealer distributes cards to the players

Looking at table (expressive)

Robot picks up its own poker cards

Cheking own card suits (manipulative)

Dealder asks the players to bet


When robot bets


When human bets




When robot bets


When human bets




When robot bets


When human bets


Table 2 Manipulation of card hand strength 1st round

2nd round

Q♣ 4♠

6♦ 10♦

3rd round

4th round

5th round

4♦ A♦

K♣ Q♦

5♣ 5♦

Table J♠ 3♦ 10♠ 7♦ K♠ 9♦ 2♦ 8♣ 9♣ 3♣ 8♥ 8♦ 6♣ 3♥ Q♥

7♠ 4♥ 2♠ 3♠ 5♥ 5♠ 10♥ 6♥ A♣ K♥

B

2♥ 7♣

A♠ A♥

8♥ Q♠

7♦ 7♣

J♥ 10♠

J♣ 8♠

6♠ Q♠

(b) Human–humanoid poker game A

8♣ A♥

2♥ 3♠

6♠ Q♦

Table 10♦ 3♥ 5♦ 6♦ 5♠ 4♦ 10♠ 9♦ 4♣ J♣ 10♣ 4♥ J♦ 7♠ 7♥ 9♠ K♣ Q♣ 2♠ A♦ 5♥ 9♥ A♠ 2♦ A♣ B

10♥ 5♣

6♥ K♥

4♠ 8♦



3.7 Measurements

(a) Human–human poker game A


J♠ K♠

2♣ 9♣

Note: A is participant’s pocket card, B is the opponent’s pocket card and table is community cards

in 2nd and 5th rounds and with the weak card hands in 1st, 3rd and 4th rounds. The strong card hands were straight and four of a kind, which means five consecutive cards and four cards of the same rank, respectively. The weak card hands indicated no pair to make them lose.

3.7.1 Nonverbal Behaviors Since the studies on nonverbal behaviors in human–robot poker game have not been sufficiently researched, we referred to the psychological research findings in the field of a human nonverbal communication related to deception. Especially, we paid attention to smile, hand movements and eye blink that have been discussed about human nonverbal behaviors in a high stake, deceptive situation that also can be seen in poker game. In this study, smile was defined as smiling as perceived by the coders, who were given no specific definition or were given a definition not involving specific AUs (e.g. corners of the mouth are pulled up’; laughing is also included) [30]. Regarding hand movement, adaptors that are movements indicating a low level of awareness such as self-touching were chosen because we assumed that self-touching will be mostly detected in poker game more than other gestures assisting speech like illustrator [31]. Self-touching was categorized as three different hand movements that are touching face, touching head and touching arm. Eye blink was

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Int J Soc Robot (2014) 6:5–15 Table 3 Result table of judging bluff and truth Consequence No.

Strong hand

Weak hand

Subject

Subject

Opponent

Opponent

1

Bluff

Bluff

Truth

Truth

2

Truth

Bluff

Bluff

Bluff

3

Bluff

Bluff

Truth

Bluff

4

Truth

Truth

Bluff

Bluff

5

Bluff

Truth

Truth

Bluff

6

Bluff

Bluff

Truth

Truth

7

Bluff

Bluff

Bluff

Bluff

8

Truth

Truth

Bluff

Bluff

9

Bluff

Truth

Truth

Bluff

10

Bluff

Bluff

Bluff

Truth

11

Truth

Truth

Bluff

Bluff

12

Truth

Bluff

Bluff

Truth

Fig. 4 Betting decision tree

3.7.3 Questionnaire defined as eyes opening and closing quickly, and eye gaze was defined as facing the other person/objects and gazing at the person/objects [30]. Especially, the eye gaze was divided into three groups; gazing at opponent, gazing at table and gazing at other. We coded the amount of four nonverbal behaviors from recorded video respectively and then calculated frequency that is a total amount of each nonverbal behavior divided by a minute. 3.7.2 Bluff Decision Betting strategies of players rely upon given circumstances such as card hand strength [32]. Hence, bluffing strategy is used according to player’s own hand strength. For instance, a player with a winning hand (strong card hand), would try to bet carefully in a non-threatening way to keep the pot growing and at the same time keep the opponent from folding early. And on the other hand, the participant with a losing hand (weak card hand) would rather try to bet in a way that the other players would assume otherwise and raise the bet taking high risks. Based on these assumptions, the player with a strong hand would avoid maximum betting and the player with a weak hand would avoid minimum betting, is considered as the bluff strategies. Figure 4 shows the all instances with betting turn in three columns and possible choices following opponent’s decision in twelve rows. We could decide whether the participant’s betting strategy is bluffing or not with the judging table as shown in Table 3. It was confirmed that all betting decisions of the participants were included in the table.

We analyzed the participants’ written responses to see how engaging the poker game with the humanoid and human experimenter was. A questionnaire was developed based on the presence as immersion, one of the six parts of Lombard and Ditton’s work [22]. This questionnaire consisted of objective type questions as well as descriptive questions that the participants answered in addition to other individual interviews. Q1 To what extent did you feel mentally immersed in the experience with Human/Genie? Q2 How involving was the experience with Human/Genie? Q3 How completely were your senses engaged in playing with Human/Genie? Q4 How relaxing or exciting was the experience with Human/Genie? Q5 How engaging was the game played with Human/Genie?

4 Experimental Results The assessment of inter-rater reliability for a total amount of the nonverbal behaviors coded by two experimenters was executed. As shown in Table 4, the calculated results using Cohen’s Kappa statistics indicated a reasonable agreement in the good to moderate or substantial ranges between two raters. In this study, because participants spent difference time of playing a poker game, all the analysis was performed in a way to transform the quantity of nonverbal behaviors into the unit of frequency. For the calculation of frequency, we


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Table 4 The inter-rater reliability for measured nonverbal behaviors (Kappa coefficient statistics) Cohen’s Kappa value H-H Poker Game

H-R Poker Game

Smile

0.67

0.68

Touching Face

0.76

0.85

Touching Head

0.73

0.68

Touching Arm

0.80

0.70

Eye blink

0.53

0.42

Gazing at Opponent

0.59

0.61

Gazing at Table

0.66

0.54

Gazing at Other

0.68

0.58 Fig. 5 Average percentage of bluff with standard error

required to separate each game round from the recorded entire episode, and used the dealer’s comments noticing beginning and end of each game round. For instance, the dealer said both “2nd round of game is started, you take first turn” at the beginning of game round and “you have straight hand, so you win!” at the end of game round. We defined playtime as a time spent for playing a poker game and breaktime as a time between rounds taken to stand by for next round. The calculated frequencies of nonverbal behavior during playtime or breaktime were utilized to analyze effects of card hand strength and the presence of an opponent, and also to examine relationship between card hand strength and human behaviors and engagement between human–human poker game and human–humanoid poker game. 4.1 Effect of the Presence of an Opponent We used Kruskal-Wallis test to evaluate the first hypothesis (see effect of opponent in Tables 5 and 6). Hypothesis 1 was supported. In Table 5, smile (χ(1) = 3.23, p < .10 in strong hand condition, and χ(1) = 3.57, p < .10 in weak hand condition) shows marginally significant difference in both hand conditions. Also, eye gaze in strong hand shows highly significant difference (Gazing at Opponent: χ(1) = 10.35, p < .01, Gazing at Table: χ(1) = 10.62, p < .01, Gazing at Other: χ(1) = 14.42, p < .001), and in weak hand condition indicates highly significant difference (Gazing at Opponent: χ(1) = 12.24, p < .001, Gazing at Table: χ(1) = 13.21, p < .001, Gazing at Other: χ(1) = 5.60, p < .05). The frequency of smile was decreased when the participants played with the humanoid. According to the followup interview, some of the participants told that they controlled over their facial expressions because they felt that the expressive head movements of the humanoid sometimes seems to observe themselves. The participants intended not to smile both in human–human and the human–humanoid poker game. This result confirms other research that people try to manage facial expressions in a deceptive situation

in which the poker game also involves deceptive interaction [33]. It is considered that the participants seriously accepted the poker game with the humanoid as much as a real poker game with human in which deceptive interaction is inherent. On the contrary, the frequency of eye gaze was increased when playing with the humanoid. The increase means that the humanoid movements draw participants’ interest and attention rather than the human opponent. This result is associated with the significant difference in eye gaze between playtime and breaktime in Table 6. According to the result in Table 6, the frequency of eye gaze in the playtime with the humanoid was significantly increased than that in the playtime with the human opponent whereas the frequency of eye gaze in the breaktime with the humanoid is significantly lower than that in the breaktime with the human opponent (Gazing at Table: χ(1) = 4.50, p < .05). This presents that the participants surely did not pay attention to the humanoid in the breaktime when the humanoid did not show any movements. Therefore, we could say that the humanoid motions fascinated the participants during the poker game. Regarding the bluff analysis as shown in Fig. 5, in weak hand condition, the bluff rate in human–human poker game (M = 67.78, S.D. = 9.23) is significantly higher than the bluff rate in human–humanoid poker game (M = 47.78, S.D. = 27.24). Only in the weak hand condition, the bluff rate shows significant difference (χ(1) = 3.99, p < .05). In this analysis, it was particularly found that while playing with the humanoid, the participants with weak hand used bluff strategies at the rate of about 50%. According to the follow-up interview, the participants assumed that the robot would not have an ability to play Texas hold’em very well after initially facing its body and movements. Therefore, they did not play carefully in the beginning of the poker game. This kind of awareness seemed to encourage the participants to play loosely in the beginning of the game. Their answers proved the assumption that the robotic appearance and mechanical movements would keep people from being involved into the game itself.

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Table 5 The difference in human behaviors between human–human poker game and human–humanoid poker game: Kruskal-Wallis test results Mean and standard deviations

Chi-Square

Human–Human poker game

Human–Humanoid poker game

Effect of hand condition

Effect of opponent

Strong

Weak

Strong

Weak

in H-H

in H-R

Strong

Weak

1.36 (1.14)

1.82 (1.58)

0.62 (0.91)

0.86 (1.23)

0.37

1.53

3.23+

3.57+

Smile: Smile Hand movement: Touching Face

0.60 (1.13)

0.83 (1.63)

0.30 (0.35)

0.40 (0.66)

1.27

0.55

0.01

0.36

Touching Head

0.10 (0.21)

0.03 (0.10)

0.25 (0.49)

0.17 (0.32)

3.32+

0.96

0.30

0.25

Touching Arm

0.10 (0.32)

0.00 (0.00)

0.15 (0.34)

0.03 (0.10)

1.00

1.30

1.30

1.00

13.21 (7.50)

11.37 (8.12)

17.35 (6.44)

14.88 (6.15)

0.37

0.57

1.46

1.65

2.40 (2.07)

1.17 (1.20)

8.35 (3.30)

6.92 (2.96)

1.06

0.58

10.35∗∗

12.24∗∗∗

7.80 (2.77)

3.93∗

9.64∗∗

10.62∗∗

13.21∗∗∗

1.67 (1.23)

12.05∗∗

14.04∗∗∗

14.42∗∗∗

5.60∗

Eye blink: Blink Eye gaze: Gazing at Opponent Gazing at Table Gazing at Other

3.30 (1.99) 0.25 (0.42)

2.30 (1.01) 0.50 (0.53)

9.20 (2.66) 2.20 (1.49)

Note: ∗∗∗ p < .001, ∗∗ p < .01, ∗ p < .05, + p < .10 Table 6 The difference in human nonverbal behaviors between playtime and breaktime: Kruskal-Wallis test results Mean and standard deviations

Chi-Square

Human–Human poker game

Human–Humanoid poker game

Effect of game state

Effect of opponent

Playtime

Breaktime

Playtime

Breaktime

in H-H

in H-R

Playtime

Breaktime

1.64 (1.37)

4.09 (2.71)

0.78 (1.15)

2.89 (1.99)

4.48∗

5.51∗

3.72+

1.04

Smile: Smile Hand movement: Touching Face

0.69 (1.18)

1.16 (2.00)

0.18 (0.24)

0.31 (0.50)

0.15

0.05

0.72

0.18

Touching Head

0.06 (0.14)

0.43 (0.98)

0.12 (0.19)

0.13 (0.40)

0.05

1.67

0.87

0.53

Touching Arm

0.04 (0.14)

0.29 (0.60)

0.04 (0.08)

0.55 (1.15)

0.53

0.18

0.85

0.16

12.11 (7.68)

16.15 (8.30)

16.03 (6.07)

23.55 (10.89)

1.85

3.02+

1.65

2.29

Gazing at Opponent

1.83 (1.60)

3.27 (2.13)

4.04 (1.73)

2.15 (1.22)

2.06

4.81∗

5.86∗

0.97

Gazing at Table

2.84 (1.41)

7.71 (2.51)

4.50 (1.56)

5.44 (0.99)

13.72∗∗∗

1.66

4.81∗

4.50∗

2.71 (1.18)

14.34∗∗∗

7.62∗∗

5.01∗

0.05

Eye blink: Blink Eye gaze:

Gazing at Other

0.37 (0.37)

3.60 (2.56)

1.05 (0.81)

Note: ∗∗∗ p < .001, ∗∗ p < .01, ∗ p < .05, + p < .10

4.2 Effect of Card Hand Strength Hypothesis 2 was supported. Touching face in human– human poker game shows marginally significant difference between strong and weak hand conditions (χ(1) = 3.32, p < .10). Gazing at table (human–human poker game: χ(1) = 3.93, p < .05, human–humanoid poker game: χ(1) = 9.64, p < .01) indicates significant difference. Also,

Gazing at other (human–human poker game: χ(1) = 12.05, p < .01, human–humanoid poker game: χ(1) = 14.04, p < .001) shows highly significant difference (see effect of hand condition in Table 5). In case of the hand movement, when the participants had weak hands, the frequency of touching face was increased and the frequency of touching head and arm was decreased. Although there is no significant effect of the hand strength,


the increased frequency of touching face partially supports the study that self-touching is increased in deceptive communication [29]. In the poker game, the participants seemed to consciously hide their facial expressions by touching their face with hand. The frequency of touching arm, mostly cross arms, shows that the participants sometimes controlled their hand movements by fixing hand position like crossing arms [34]. The frequency of eye gaze in strong hand condition was significantly increased, meaning that the time of fixed gaze was decreased. Especially, the frequency of gazing at other shows the most significantly increase than gazing at opponent and gazing at table. It presents that the participants with their undoubted winning hand felt so relaxed and seemed to concentrate on other things, rather than focusing on the game playing. The another result was obtained from the bluff decision that is dependent on the card hand strength (human–human poker game: χ(1) = 14.65, p < .001, human–humanoid poker game: χ(1) = 3.80, p < .10). The bluff decision was affected by card hand strength except for the initial round when the participants did not try to bet deceptively. Meanwhile, it is expected that if the subjects would get hold of Texas hold’em much more with technical skills, the effect of card hand strength would be accordingly decreased over time. 4.3 Relationship Between Card Hand Strength and Human Behaviors The binomial logistic regression analysis with backward stepwise was applied to see which of predictors would best account for the relationship between hand strength and human behaviors. Independent and dependent variables were set as follow; dependent variable (given situation) is the card hand strength of the participants, and independent variables (decision and interaction) are the bluff decision and nonverbal behaviors of the participants. From this analysis, we learned that the bluff rate (β = −0.86, p < .05), smile (β = −0.62, p < .10), blink (β = 0.76, p < .05) and gazing at other (β = 0.60, p < .10) are the best predicted variables. Since the result in Table 7 supported Hypothesis 3, we could say that the given situation of the participants has relation to their decision and nonverbal interaction.

13 Table 7 Result table of binomial logistic regression analysis Coefficient

OR

p-value

95% C.I for OR Lower

Upper

Bluff

−0.86

0.43

0.02∗

0.21

0.88

Smile

−0.62

0.54

0.06+

0.28

1.03

Blink

0.76

2.13

0.03∗

1.07

4.23

Gazing at Other

0.60

1.82

0.08+

0.94

3.52

Constant

−0.94

0.39

0.53

Note: ∗ p < .05, + p < .10 Model χ test: p < 0.01

(χ(1) = 14.34, p < .001) show highly significant difference. In the human–humanoid poker game, gazing at opponent (χ(1) = 4.81, p < .05) and gazing at other (χ(1) = 7.62, p < .01) show significant difference. Considering that the increase in frequency of eye gaze means that the time of fixed gaze is decreased, the participants in the human– human poker game fixed their gaze on the table to observe the game situation while frequently looking at the humanoid in the human–humanoid poker game. Hypothesis 4 was supported. The result derives another interaction pattern change from the smile between playtime and breaktime. The frequency of smile between playtime and breaktime shows a significant difference (human–human poker game: χ(1) = 4.48, p < .05, human–humanoid poker game: χ(1) = 5.51, p < .05). It is supposed that the participants controlled their facial expression more while engaged in the game playing with tension. 4.5 Measurement of Engagement The qualitative study about engagement was performed using Mann-Whitney U test. The result shows no significant difference between human–human and human–humanoid poker game (Q1: z = −0.87, Q2: z = −0.91, Q3: z = −0.08, Q4: z = −0.27, Q5: z = −0.32) as shown in Fig. 6. Hypothesis 5 was not supported. Although the humanoid performed simple head and arm movements, the participants evaluated the similar level of engagement. This result presents that the humanoid kept the participants engaged in the poker game in a different way of interaction pattern with the human–human poker game.

4.4 Effect of Game State: Tension vs. Release 5 General Discussion and Conclusion The effect of game state in terms of tension and release was examined using Kruskal-Wallis test. This analysis, in particular, focuses on the frequency of eye gaze between playtime and breaktime. In human–human poker game, gazing at table (χ(1) = 13.72, p < .001) and gazing at other

5.1 Contributions It has been found that the opponent presence and card hand strength were significantly effective on the participant’s bet-

14

Fig. 6 The average ratings with standard error intervals with respect to the question items for engagement

ting decision and nonverbal behaviors. Besides, the relationship between hand strength and human behaviors has been analyzed and the human opponent model that incorporates both mathematical and psychological terms has been discovered. Also, the engagement was evaluated through the qualitative study and analyzed with the frequency of eye gaze and smile. Through our study, two prominent considerations in designing a socially interactive poker playing humanoid were drawn. The first one is to observe human smile and eye gaze in the human–humanoid poker game. These can be used for measuring how engaged human player is in socially interacting with the robot. The participants in human–humanoid poker game preferred to interact with the robot more than in the human–human poker game. It does not mean that they did not play the game with the robot attentively because they attempted to hide intentions by controlling over their facial expressions. On the other hand, the participants paid attention to play the game more than in the human–humanoid poker game. We found that the effect of hand strength in the human–human poker game was highly significant while being marginally significant in the human–humanoid poker game. It is inferred that our humanoid significantly evoked different nonverbal interaction and difference of betting strategy pattern. Our results embrace the importance of physical embodiment that has been explored in game tasks [35]. Even machine-like appearance facilitated social interaction with the poker playing humanoid through employing anthropomorphism that is motivated to design a system that functions in physical and social space and make its mechanism to facilitate social interaction with people [36]. From social interaction perspective, our poker-playing robot can be utilized as a social partner. Although the benefit of attracting people to socially interact with it differentiates itself from other virtual agents in video game and networked game, it will successfully encourage a human social behavior and keep people motivated to play games with it.


The second consideration is to model human opponent based on betting decision and the nonverbal interaction altogether. We expect that this model will enable the poker playing robot to perform in a same way of understanding human player as discussed. Consider the following example: the robot assesses its opponent hand strength in order to make its betting decision. It often makes wrong decision because of imperfect information of the game the intentional nonverbal behaviors of the opponent. To resist being defeated in succession, the robot modifies the opponent model parameter by learning the game situation, opponent’s intentions, etc. Similar research modeled robot deception to capacitate human-like survival skills in situations involving conflict [37]. This robot recognizes a situation and selects deceptive strategy to reduce the chance of being found. Likewise, the model of human opponent that we investigated in this study will provide the potential of adapting the game playing humanoid to human social poker game. As a final remark, discovering commonalities and differences of the human perceptions on human and robot guides us to learn how to make the robot to be perceived as it acts like a human. Likewise, in order to be a social partner, the game playing humanoid must be perceived as it interacts like human. Therefore, we conducted comparative study to see how differently people respond toward human and robot. We hope that this study contributes to not only the practical design of socially interactive game playing robots, but also the theoretical approach on the realization of behaving in a similar way of human doing in a game playing. 5.2 Future Works In the future, further psychological research should be conducted with real money in long-term in order to investigate the effects of opponent presence and hand strength and engagement in more real situation. Furthermore, it should be investigated how robot behavior affects people to interpret it in the poker game with the developed humanoid platform. And the proposed decision making structure of the humanoid playmate will be verified to be able to understand and learn human behaviors while playing the poker game autonomously. Acknowledgements This work is partially supported by Grand-inAid for Scientific Research and Global COE Program on “Cybernetics: fusion of human, machine, and information systems” by MEXT, Japan.

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Min-Gyu Kim He is currently a Senior Researcher of Interaction Science Research Center at Sungkyunkwan University, Korea. He received the B.E. in Electronics Engineering and M.E. in Mechanical Engineering from Korea Aerospace University, Korea, in 2003 and 2005 respectively. He also received Ph.D. in Intelligent Interaction Technologies from University of Tsukuba, Japan in 2012. He was a Postdoctoral Researcher in Artificial Intelligence Laboratory at University of Tsukuba, Japan in 2012. He is interested in Robotics, Human Robot Interaction and Cognitive Engineering. Kenji Suzuki He is currently an Associate Professor of the Faculty of Engineering, Information and Systems, and Principal Investigator of Artificial Intelligence Laboratory, University of Tsukuba, Japan. He also belongs to the Center for Cybernetics Research, and he is a JST PRESTO Researcher, Japan Science and Technology Agency, Japan. He received the B.S. in Physics, M.E. and Dr. Eng. in Pure and Applied Physics from Waseda University, Tokyo, Japan, in 1997, 2000 and 2003 respectively. He was a visiting researcher at the Laboratory of Musical Information, University of Genoa, Italy from 1997 to 1999, and also at LPPA, Laboratory of Physiology of Perception and Action, at the College de France, Paris in 2009. His research interests include Robotics, Assistive and Rehabilitation Robotics, Human Robot Interaction, Augmented Human, and Affective computing. He is currently a member of IEEE and ACM.