Intelligent Narrative Technologie and Social Believability in Game: Paper from the AIIDE 2015 Joint Workhop Culturally Appropriate Behavior in Virtual Agent: A Review Mahael Al-Saleh and Daniela M. Romano Department of Computer Science, Univerity of Sheffield, UK {mmalaleh1, D.Romano}@heffield.ac.uk Abtract Social behavior cannot be conidered without the culture in which it i expreed. The following i a concie tate of the art review of intelligent virtual agent diplaying culturally appropriate behavior in game and eriou game. In particular, it focue on agent diplaying peronality and emotion, and their ability to engage in ocial interaction with other. The relationhip between the character external repreentation and the cultural believability i highlighted; and the internal and viual apect of the current tate of the art agent are dicued. A chematic view of the literature and the element required for embodied culturally appropriate agent i preented, offering opportunitie for future reearch. behavior. In contrat, other application (e.g., tutoring and coaching agent) neceitate more human-like agent, where the character appearance and behavior might require a more accurate expreion of emotion, a peronality, and other apect enabling ocial interaction (Gupta, Romano, and Walker 2005; Louchart et al. 2004; Romano 2005; Roi, Pelachaud, and Poggi 2004). Thi paper aume that exhibiting a culturally appropriate behavior increae the character believability, and dicue how thi ha been achieved to date in literature from the perpective of both computer cience and cro cultural pychology. Introduction Culturally appropriate behavior i not genetically programmed, but i intead learned from direct teaching, or by oberving and interacting with other. For example, language i one of the primary abtract artifact tranmitted extra genetically. Thi paper provide a review of how culturally appropriate behavior can be achieved in ynthetic agent and offer a concie overview of the relevant literature. Bate (1994) decribe believable character a thoe delivering the illuion of life. In order to achieve thi illuion for culturally appropriate agent, many element mut be conidered, including the character ability of perceiving ynthetic character and non-character in the environment, alo defined a ocial intelligence, and the ability to generate a repone congruent with it behavioral, viual, and cultural apect. In particular, the level of detail of the viual repreentation mut match the perceived ocial intelligence for the character to be believable and for it abilitie to meet the viual apect requirement (Romano 2005; Romano and Wong 2004; Romano et al. 2005; Shaarani and Romano, 2006; 2007; 2008; Burkitt and Romano 2008; Gupta, Walker, and Romano 2008; 2010). For example, cartoon are an attractive olution for ome application in which the main goal i to portray tereotyped Copyright 2015, Aociation for the Advancement of Artificial Intelligence (www.aaai.org). All right reerved. Background on Culture and Synthetic Culture The relevant literature maintain many different definition of culture, which vary according to the field of tudy. Hoftede ha tudied the feature that allow u to dicern different culture (Hoftede 2001), defining culture a: The collective programming of the mind that ditinguihe the member of one group or category of people from an- other (Hoftede 2001, page 9). How people think, feel, and act i baed on what they have learned from other in the ociety, and learnt pattern of behavior can appear in the form of value or can be oberved in the form of ritual, heroe, and ymbol (Macrenha, Enz, and Paiva 2009). Mot of the reearch in culturally appropriate agent to date ha been built around five model (Hoftede 2001). Thee have been ued differently in each architecture that implement agent model. For example, a reported in Table 1, Macrenha, Enz, and Paiva (2009) ued thee in two part of their model: meauring goal utility and emotional appraial; involving only the two of individualim and power ditance. On the other hand, Rehm et al. (2007) found a correlation between all five and their effect on four agent characteritic: overall activation, patial extent, peed, and power. Hoftede five of any culture are a follow: 69
Power ditance: concern the acceptance of an unequal ditribution of power in a given ociety. Democratic ocietie are conidered low power ocietie becaue power i ditributed equally. Converely, in high power ocietie, people accept and repect the concentrated power of a few. Individualim and collectivim: refer to whether priority interet i given to each individual or to the group. Societie with a higher priority to the individual require that people are reponible for their individual elve and the people cloe to them. Maculinity veru Femininity: examine the trength of maculine value compared to feminine value in ociety. Uncertainty avoidance: the level of tolerance to uncertainty in the ociety, in other word, the feeling of being threatened by unpredictable ituation. Short-Term veru Long-Term Orientation: the importance of future veru pat and preent. Another approach ued in the literature decribe culturally appropriate behavior by directly mapping out how the ocial relationhip typically take place within the group. Thi paper identifie the direct mapping of culturally appropriate behavior a ocial interaction rule (SIR). For example, when an agent i intructed on how to greet omeone, their action culturally pecific; if following the Japanee culture, their action would entail a bow, while if baed on a wetern culture, a handhake would be required. Culture-Adaptive Agent in Virtual Environment Virtual environment and eriou game provide opportunitie for people to learn ocial and behavioral apect (Bainbridge 2007). Some well-known environment ued for reearch include Second Life and World of Warcraft. Thee allow for the creation of controlled environment in which the uer are capable of experiencing different ituation and culture (Macarenha, Silva, et al. 2013). The embedding of cultural concept in the deign of ynthetic character i very important in order to get uer to believe thee character are alive, to timulate interaction, and to provide an experience imilar to that of the real world (Jan et al. 2007). Believable virtual character help achieve one of the main objective of human-computer interaction, which i to make the uer feel that they are interacting with a human, rather than a ynthetically generated being (Loyall and Bate 1997). (2005) ignificant tudy in thi domain conit of ten year of reearch with over 1400 participant who played imulation game uing ynthetic culture. Each ynthetic culture i ued a a cript for the role player, and wa derived from the five of culture from model. He found that participant reported the game-play experience a an eye-opener that increaed their undertanding of cro-cultural conflict. He alo oberved that the participant intrinic qualitie uch a peronality, human nature (i.e., the baic ocial impule that drive human behavior uch a ex, affiliation, and dominance), and the participant own culture influenced the manner in which they played the game. Architecture for Culturally Appropriate Agent The literature reveal a trong interet in developing culturally adaptive agent in order to improve ytem performance and uer atifaction (O Neill-Brown 2007; Wagner et al. 2006). Importantly, thee ytem differ baed on whether the cultural apect are relevant for verbal behavior (e.g., Kim et al. 2009) or non-verbal behavior (e.g., Blanchard et al. 2015) or both (e.g., Deaton et al. 2005). The deign of agent in virtual world typically target a pecific culture, regardle of whether thi intention i explicit or not. If the deign i later determined to be ued within another culture, ignificant effort i required to adapt the agent to the new target culture (Jan et al. 2007). Alternatively, it i poible in the early tage of the deign to conceive of an agent with a modular architecture in which culture i an element that can be replaced or extended to accommodate another culture. Table 1 below lit ome example of agent architecture and application that include cultural apect a part of the agent non-verbal behavior deign. Figure 1 below diplay a chema highlighting how culture can be conidered an independent module from the ret of the agent architecture. Some of the architecture have been created a an extenion of an exiting emotion and/or peronality model, a a way to increae the character believability and it reaction congruence within the virtual environment. For example, Macrenha, Enz, and Paiva (2009) created an agent baed on the FAtiMA architecture for emotional agent (Dia et al. 2014) and the PSI theory of emotion (Dörner 2003). Additionally, Nazir et al. (2009) propoed an agent baed on the PSI emotional model and the Big Five Factor model of peronality (Digman 1990). Other model are an extenion of agent teamwork (e.g., Jan et al. 2007; Pynadath and Marella 2005). The cultural apect are motly created uing model (Roi, Pelachaud, and Poggi 2004), (Macrenha, Enz, and Paiva 2009), (Rehm et al. 2007), (Nazir et al. 2009), while Jan et al. (2007) conider non-verbal communication parameter uch a proxemic (i.e., the patial ditance between individual), gaze, and turn taking. Pynadath and Marella (2005) model allowed the uer to create their own belief and preference. 70
Author/ - Year Theorie & Model Synthetic Culture Scenario / Aim Evaluation Technique Cultural apect evaluated AI technique ued (Hoftede 2005) (Macrenha, Enz, and Paiva 2009) model Integration to autonomou agent architecture FAtiMA (Fearnot Affective mind architecture) PSI theory of emotion Uer create culture Teach teenager the difference between culture Multi-player environment Two video-baed cenario differ in ritual repreentation Software imulation evaluated by uer (ORIENT) How culture are created Greeting, welcoming and dinner ritual Not reported FAtiMA ue goal election baed on a goal utility function Emotional appraial and reaction Planning capabilitie (Jan et al. 2007) (Roi, Pelachaud, and Poggi 2004) (Rehm et al. 2007) (Pynadath and Marella 2005) (Nazir et al. 2009) Extenion to work on group imulation ytem Expanion of GRETA (Intelligent Believable Embodied Converational Agent) No background theory or model ued Extenion to the Com-MTDP model of agent teamwork i baed on the theory of mind Baed on PSI theory of emotion and the Big Five peronality trait Social interaction rule and ocial interaction rule Social interaction rule Converation in three different culture: Anglo American, Spanih-peaking Mexican and Arab culture GRETA role i to engage uer in natural converation (Project Magicter) Detect uer culture from overall activation, patial extent, peed, and power of movement Uer create own et of agent with peronal preference, relationhip with other entitie, private belief, and mental model Ue reearcher own lit of key behavior of people in the culture conidered, and poitive and negative word Video-baed cenario Converation between the uer a patient and the agent a doctor to decribe the appropriate therapy. The uer culture wa detected uing Wii enor and mapped onto the agent behavior and compare for imilarity Not reported Video-baed cenario Proxemic, gaze, and turn taking Appropriate verbal and non-verbal communication, including facial expreion, head movement, body poture and/or geture Overall activation, patial extent, peed, and power of movement Uer built chool bully cenario Identify key behavior in the culture No AI technique ued to upport agent behavior There i no pecific AI element added for cultural adaptation, but GRETA ha a Litener Intent Planner component (Niewiadomki et al. 2009) A Bayeian network wa ued a network of probability to link between feature in each culture Partially obervable Markov deciion problem (POMDP) ued to olve problem baed on agent' preference and belief Not reported Table1 Agent architecture including cultural apect The repreentation of i not ufficient to portray all apect of a culture, and the author himelf claim that it i neceary to involve cultural ymbol and ritual in the agent deign (Hoftede 2001), which can be repreented a a cultural profile. Symbol are any geture, word, and picture that have pecial meaning in the conidered culture, wherea ritual hape the manner in which ome ocial activitie are undertaken. The mot common manner to evaluate a computationally portrayed behavior i to create video cenario in a pecific context and ak the uer to ae the cenario cultural repreentation, or to determine the difference between the culture portrayed. Some of thee agent model have been repreented a interactive application, either to create dynamic cenario baed on the uer culture (Rehm et al. 2007) or cultural training application (e.g., Macrenha, Enz, and Paiva 2009; Thovuttikul et al. 2011). Different AI technique have been added to thee model to upport the agent behavior. Each model ha it own approach on how to employ thee technique to reach model goal in the repreentation of cultural agent. Some of thee technique have been inherited from the original architecture, which have been extended. For example, the model provided by Macrenha, Enz, and Paiva (2009) include different AI element from FAtiMA, like goal utility function, emotional appraial, and reaction, a well a the ability to plan for future action. Roi, Pelachaud, and Poggi (2004) ued AI component already preent in the embodied converational agent GRETA and extended it capabilitie. Finally, ome of the architecture have their own independent intelligent component (e.g., Rehm et al. 2007; Pynadath and Marella 2005; Nazir et al. 2009). Table 1 above provide a ummative overview of the main conideration required when deigning culturally appropriate behavior for an agent together with the main feature included in the agent architecture and the role of cultural apect in the deign. 71
Figure 1 Schema for Culturally Appropriate Embodied Agent Culturally Appropriate Behavior in Agent Given the finding in the literature, it appear that a culturally appropriate agent might need ome or all of the element decribed in the chema in Figure 1. It i not neceary for an agent to conider all the component illutrated in Figure 1, but uch component hould be determined baed on the agent architecture context of ue and application. In particular, we have highlighted the need for the culture module to be independent form the internal and external element of the agent. Thee element can be divided into two part, where ome are internal element drive the external repreentation of the agent. Internal element are related to the computational apect of generating agent behavior to provide a high degree of ocial interaction. Thee can be achieved by conidering the pychological factor that play important role in driving ocial interaction: emotion and peronality. There are two main emotional theorie cited in the literature: OCC (Ortony, Clore, and Collin, 1988) and PSI (Dörner 2003). OCC ha been embedded in everal emotional model, uch a FAtiMA (Dia, Macarenha, and Paiva 2014) and BASIC (Romano et al. 2005). PSI i part of ome propoed model that have integrated culture into their deign, uch a Nazir et al. (2009) and Macrenha, Enz, and Paiva (2009). The five factor model of peronality i the mot ued model integrated into agent architecture. Recognizing the difference between agent culture during the ocial interaction only from the computational apect i difficult. Conequently, the agent behavior generation often illutrate the difference through an embodiment of external repreentation of the agent behavior. Example of external repreentation that manifet agent interaction have been pointed out by Vinayagamoorthy et al. (2006). In particular, the author report the need to conider pecific clae of non-verbal behavior, uch a: Emblem: refer to the tandardized geture and ignal that are well undertood in a particular culture. They are ued intentionally and conciouly in ituation when verbal communication i not poible or to augment a verbal concept uing abtract repreentation of the concept, e.g., a geture that repreent a wear word. Geture in the Southern Italian culture have been developed to bridge the gap acro the variou local dialect poken in the land, explicating concept uing commonly agreed upon emblem. Illutrator: are ignal that are created on the pur of the moment, and while are not often pre-conceived, are till voluntary, have a clear meaning, and are ued to further explain the peech. An example might be uing a geture to how the hape of an object. Concluion A dicued earlier in thi paper, conidering cultural apect can increae the believability of ynthetic ocial agent. Culture drive human internal expreion and emotion a well a their phyical activitie and appearance. Example of thee external repreentation were dicued in addition to a tate of the art review on the model and method ued to generate cultural expreive virtual character, conidering in particular the computational generation of behavior and the non-verbal behavioral apect; the latter depend on the pecific culture and interperonal relationhip in the context. Moreover, if the agent belong to a pecific ocial group or have to interact within a group, the diplay of uch behavior allow human uer to recognize the culturally appropriate emotion and peronalitie portrayed in a more believable manner. Thi explain the involvement of the cultural and teamwork model a a central control and independent component in culturally believable agent architecture. Acknowledgment Thi reearch ha been upported by the Saudi Minitry of Education, King Saud Univerity, and conducted at Univerity of Sheffield UK. 72
Reference Bainbridge, W. S. 2007. The Scientific Reearch Potential of Virtual World. Science (New York, N.Y.) 317(5837): 472 76. Bate, Joeph. 1994. The Role of Emotion in Believable Agent. Communication of the ACM 37(7): 122 25. Blanchard, Emmanuel G, W Lewi Johnon, and Amy Ogan. 2015. Third International Workhop on Culturally-Aware Tutoring Sytem. (June). Burkitt, M., and Romano, D. M. 2008. The Mood and Memory of Believable Adaptable Socially Intelligent Character. In Intelligent Virtual Agent, Springer Berlin Heidelberg. 5208 LNAI: 372 79. Deaton, J.E. et al., 2005. Virtual environment cultural training for operational readine (VECTOR). Virtual Reality, 8(3), pp.156 167. Dia, J., Macarenha S., and Paiva, A. 2014. Fatima Modular: Toward Agent Architecture with a Generic Appraial Framework. In Emotion Modeling, Springer International Publihing 44-56. Digman, J. M. 1990. Peronality Structure: Emergence of the Five Factor. Annu. Rev. Pychol 41: 417 40. Dörner, D. 2003. The Mathematic of Emotion. In Proceeding of the Fifth International Conference on Cognitive Modelling, 75 79. Bamberg, Germany. Gupta, S., Walker, M. A. and Romano, D. M. 2008. Uing a Shared Repreentation to Generate Action and Social Language for a Virtual Dialogue Environment. Proceeding of the AAAI Spring Sympoium on Emotion, Peronality and Social Behaviour,49-54, Gupta, S., Romano D. M. and Walker, M. A. 2005. Politene and Variation in Synthetic Social Interaction. In H-ACI Human- Animated Character Interaction Workhop in conjunction with the 19th Britih HCI Group Annual Conference. Hoftede, G. J. 2005. Role Playing with Synthetic Culture: The Evaive Rule of the Game. In Proceeding of the 9th International Workhop of the IFIP, 49 56. Hoftede, G. 2001. Culture Conequence: Comparing Value, Behavior, Intitution, and Organization acro Nation. Second. Buine & Economic. Jan, D., Herrera, D., Martinovki, B., Novick, D., Traum, D. 2007. A Computational Model of Culture-Specific Converational behavior. Intelligent Virtual Agent Springer Berlin Heidelberg: 45 56. Kim, Julia M et al. 2009. BiLAT: A Game-Baed Environment for Practicing Negotiation in a Cultural Context. International Journal of Artificial Intelligence in Education 19(3). Louchart, S., Romano, D., Aylett, A. and Pickering, J. 2004. Speaking and Acting - Interacting Language and Action for an Expreive Character. In Proceeding of the IVEVA 2004 Workhop. Technical Univerity of Aachen (RWTH). Loyall, B., and Bate, J. 1997. Peronality- Rich Believable Agent That Ue Language, Proceeding of the Firt International Conference on Autonomou Agent (Agent 97), 106 13, ACM. Macarenha, S., Dia, J., Afono, N., Enz, S., & Paiva, A 2009. Uing Ritual to Expre Cultural Difference in Synthetic Categorie and Subject Decriptor. In Proceeding of 8th Int. Conf. on Autonomou Agent and Multi-agent Sytem (AAMAS 2009), 305 12. International Foundation for Autonomou Agent and Multiagent Sytem. Macarenha, Samuel Francico et al. 2013. Can I Ak You a Favor? - A Relational Model of Socio-Cultural Behavior. 12th International Conference on Autonomou Agent and Multiagent Sytem: 1335 36. Nazir, A., Enz, S., Lim, M. Y., Aylett, R., and Cawey, A.. Culture-Peronality Baed Affective Model. AI and Society 24(3): 281 93. Niewiadomki, R., Bevacqua, E., Mancini,M and Pelachaud, C. 2009. Greta: An Interactive Expreive ECA Sytem.. In Proceeding of the 8th International Conference on Autonomou Agent and Multi-agent Sytem-Volume 2. Inter-national Foundation for Autonomou Agent and Multiagent Sytem: 1399 1400. O Neill-Brown, P. 2007. Setting the Stage for the Culturally Adaptive Agent. In Proc. AAAI Fall Sympoium, http://citeeerx.it.pu.edu/viewdoc/ummary?doi=10.1.1.54.613. Pynadath, D. V., and Marella, S. C. 2005. PychSim: Modeling Theory of Mind with Deciion-Theoretic Agent. In IJCAI International Joint Conference on Artificial Intelligence, 1181 86. Ortony, A., Clore, G. and Collin, A. 1990. The Cognitive Structure of Emotion. Cambridge univerity pre. Reeve, Byron, and Na, C. 1998. The Media Equation: How People Treat Computer, Televiion, and New Media Like Real People and Place, 19 36.Cambridge Univerity Pre. Rehm, M., Bee, N., Endra, B., Winer, M., and André, E. 2007. Too Cloe for Comfort?: adapting to the uer' cultural background In Proceeding of the International Workhop on Human-Centered Multimedia - HCM 07, Germany, 85-94:ACM. Romano, D. M., Sheppard, G., Hall, J., Miller, A., and Ma, Z 2005. BASIC: A Believable Adaptable Socially Intelligent Character for Social Preence. In PRESENCE 2005, the 8th Annual International Workhop on Preence, Univerity College London, London. Romano, D. 2005. Synthetic Social Interaction. In H-ACI Human-Animated Character Interaction Workhop the 19th Britih HCI Group Annual Conference Napier Univerity, Edinburgh. Romano, D. M., and Wong, A.K.L. 2004. Peronality Model of a Social Character. In In 18th Britih HCI Group Annual Conference. Romano, D. M. 2010. Look, Emotion, Language and Behavior in a Believable Virtual Companion. In In Cloe Engagement with Artificial Companion: Key Social, Pychological, Ethical and Deign, ed. Yorick Wilk. John Benjamin Publihing, 315. Roi, F., Pelachaud, C. and Poggi. I 2004. Trancultural Believability in Embodied Agent: A Matter of Conitent Adaptation. Agent Culture: Human-Agent Interaction in a Multicultural World: 75 106. Shaarani, A. S, and Romano, M. D. 2007. Perception of Emotion from Static Poture. Springer-Verlag Berlin Heidelberg: 761 62. Shaarani, A.S., Romano, D. M. 2006. Baic Emotion from Body Movement. In The Firt International Sympoium on Culture, Creativity and Interaction Deign, HCI. Shaarani, A. S., and Romano M. D. 2008. The Intenity of Perceived Emotion in 3D Virtual Human. In Proceeding of the 7th 73
International Joint Conference of AAMAS, 1261 64, Portugal: International Foundation for Autonomou Agent and Multi-agent Sytem. Thovuttikul, S., Lala, D., Ohahi, H., Okada, S., Ohmoto, Y., and Nihida, T. 2011. Simulated Crowd : Toward a Synthetic Culture for Engaging a Learner in Culture-Dependent Nonverbal Interaction. In In 2nd Workhop on Eye Gaze in Intelligent Human Machine Interaction, Stanford Univerity, USA. Wagner, Daniel, Mark Billinghurt, and Dieter Schmaltieg. 2006. How Real Should Virtual Character Be? Proceeding of the 2006 ACM SIGCHI international conference on Advance in computer entertainment technology - ACE 06: 57. Vinayagamoorthy, V., Gillie, M., Steed, A., Tanguy, E., Pan, X., Loco, C., and Slater, M. 2006. Building Expreion into Virtual Character Building Expreion into Virtual Character. 74