Emotional Intelligence Engine for Serious Games

Emotional Intelligence Engine for Serious Game Hooman Aghaebrahimi Samani (PhD Student) Mixed Reality lab, NUS Graduate School for Integrative Science and Engineering, National University of Singapore Doros Polydorou (PhD Student) Mixed Reality lab, National University of Singapore Tim Marsh (Assistant Professor) Mixed Reality lab, Communication and New Media, Faculty of Arts and Social Sciences, National University of Singapore Adrian David Cheok (Associate Professor) Mixed Reality lab, National University of Singapore and Keio University Keywords: Serious Game, Games for Learning, Emotional Intelligence, Artificial Intelligence, Affective Computing Abstract To improve the learning factor of serious games, we introduce an affection layer which improves the emotional intelligence of a game character. As the components of that layer we propose modules which can be integrated in a game engine in order to enhance the verisimilitude of the virtual world. The proposed architecture can be integrated in several games to improve their emotional abilities which can lead to developing believable characters in the game environment. We believe that such ability increase the motivation for the user to learn since possibilities and situations would be much pragmatic. Introduction One of the main purposes of serious games is learning. In this paper, we propose a layer in game engines that could provide more realistic behavior in the serious game environment which can influence the improvement of learning directly. There are many (serious) games which deal with social issues which try to promote empathy in a player. Social interactive games such as Sims(2000) and Marriage(2006) let players interact with virtual characters Davies, M. (2007). Games in the market are now becoming more and more emotionally mature aiming at connecting with the player. Bosser, A.(2007), Thawonmas R. (2007), Ma, L. (2007), Salem, B. (2007), Winn. B. (2009) and Levy, D. (2007). The new trend in games is to try and inject emotions into games. Freeman, D. (2003) The lack of emotional attachment in gaming is a notion widely accepted. Few games, if any, manage to make the player feel empathy for the character that they are controlling or sympathy for the characters around him. Even though that can be accounted to a number of reasons, a big setback is the fact that virtual worlds do not page 1 Emotional Intelligence Engine for Serious Game feel real. The characters which share this world with the player are lifeless, with prefixed static behaviors and actions. An experienced player knows and has grown accustomed to expect just that. NPC’s are there either to provide support, information, give guests or sell items. For example, no matter how many times you ask them the same thing or how badly you treat them; they will still be there to do their assigned task. This expectation automatically lowers the believability of the characters and the world, making the player less concerned about the consequences of his/her behavior, thus subsequently not caring about the players they are meant to feel empathy and sympathy for. In order for games to be taken seriously and used as a tool for education, this problem must be remedied. Salen, K. (2004), define play as the navigation of a suite of choices (i.e. decisions), where each decision leads to an action that has a discernable outcome. Currently, the choices that a player makes in a virtual world are reflected in results which are non realistic but limited by the Artificial Intelligence of the game. This kind of suspension of disbelief might be acceptable for commercial games but not for serious games, especially if their creators aim to get them approved as a successful learning medium. Creating a world where the people in it have feelings and act according to them can motivate and relate to their emotional Intelligence. Salovey, P. (1990) defined Emotional Intelligence as “the ability to monitor one's own and others' feelings and emotions, to discriminate among them and to use this information to guide one's thinking and actions.” This paper argues that Emotional Intelligence can have a significant influence on how successful serious games can be as a learning tool. Implementation of Emotional Intelligence in Serious Games The emotional intelligence platform is a system that aims to make a virtual world more believable by simulating human emotions and applying them to NPC’s. Looking specifically at Serious Games, the platform can be applied as a base in the creation of a virtual environment. Emotions will play an integral part of how the world functions and offer a great sense of unpredictability to it. This will make the virtual worlds more believable for the player, offer more accurate simulated results and even oppose the general misconception that games are just for fun and can offer nothing more than that. Each NPC is assigned with certain random characteristics and an area of influence. As the player explores the world and interacts with the NPC’s these characteristics change according to the actions of the player, whenever he/she is in their area of influence. Furthermore when that NPC whose characteristics changed enters the area of influence of another NPC those player influences are transferred over to second NPC as well. To make things easier to understand, let’s take a simple example. Let’s imagine a game that aims to teach the culture of a country. The player is free to roam in a city market and enters a shop. In the shop there are two NPCs, a female and the shop owner. The player chats politely with the shop owner. The female NPC is in the vicinity of the conversation, therefore because the player was polite, her likability towards the player increased as well. The female NPC then moves away from the shop, entering a second shop. Since she will enter the area of influence of the shop owner of the second shop, the likability influences towards the player are transferred to the second shop owner as well. If the player now enters the second shop, since the shop owner already “heard” positive comments from the female NPC about him, he can offer him a small discount or different dialogue options. We are of course making the assumption here, that the second shop owner has the character page 2 Emotional Intelligence Engine for Serious Game trait of trusting people easily and the female NPC has the tendency to talk openly to everyone. Since in the beginning all these character traits were distributed randomly nobody can predict how the situation will play out. Depending on how the player treats the NPCs, there will be a different reaction. The player by realizing that, will no longer treat the people in the world with contempt and will start thinking about their feelings as well. This way the player can learn the importance of good behavior and politeness. Continuing on the example of the cultural game, let us now assume that we want to use the same platform for a different country. In this country the people are less open and more private with their lives. By adjusting some basic values, we can easily control the original random character trait assignment to reflect the change. Therefore now the engine will be more conservative on the values it will assign to each NPC. The aim of this system is to enhance the verisimilitude of the virtual world. Crucial game play objectives which are vital for the continuation of the story will of course not be left at random. They will still be scripted in the world by the game designer. Compared to older systems the Emotional Intelligence Platform can also offer new creative perspectives without the need of scripting everything. For example let us imagine that the objective of the player is to get inside a locked door. The guard standing outside has the keys and in order to let the player in he must have a high trust value towards the player. In order for that trust to be raised, the guard asks the player to perform a task for him. If however, the player did something influential to an NPC who has access to the guard, then the trust level towards the player might already be high, thus offering to the player another way in without having to complete the task. Since the influences platform is already set up, the game designer will only have to script the behavior of the guard when the trust level is high enough, saving valuable time from having to script the event from the beginning. Furthermore, this platform can educate players on the importance of honesty, trust and friendship and show that every action can have an effect on the world around you. Development of Emotional Intelligence Our proposal is that a character in a game should be equipped with an intelligent emotional module which controls the affective behavior of character in the game. Emotional properties are mathematically modeled to generate a platform for overall feelings in characters. That model would control the affective state of the character. In this section we describe the 3D emotional space and the mechanism for transition in that system. We also explain the possibility of involving artificial endocrine system in that emotional module. We consider interaction area around each agent within the game engine. When two agents locate in the interactive zone, emotional expression values of each agent would be transferred to the corresponding agent. Emotional Modelling in the Game Engine The internal emotional property of a character in a game can be modeled as an affective state system. In this section we describe an affective state model and propose a systematic method for handling changes in such model. page 3 Emotional Intelligence Engine for Serious Game Affective State module Besides realizing the emotional expressions of the interaction, the system may develop the internal state of the character to handle the overall emotional situation. Different methodologies have been used for dealing with the internal state. Nourbakhsh, I. (1999) introduced a fuzzy state machine based system which was developed through a series of formative evaluation and design cycles. Schulte, J. (1999) explains the design of a simple state machine that produces four basic moods. Kim, J. ( 2008) has proposed a multi-objective evolutionary generation process for artificial creatures specific personalities (MOEGPP), where the dimension of the personality model is defined as that of optimization objectives. Hashimoto, H. (1998) used the finger blood pulse fluctuations for developing the online system to estimate a driver’s internal state. A large number of studies employed the OCC model proposed by Ortony, A. (1988) as the fundamental model of emotion. For example, C. Bartneck. . (2002) has integrated that platform for modeling the emotions in the embodied character. The model in C. Breazeal. (2003) focuses on the role of emotion and expressive behavior in regulating social interaction between humans and expressive anthropomorphic robots. Velasquez J. (1997) presents a distributed and computational model which offers an alternative approach to model the dynamic nature of different affective phenomena, such as emotions, moods and temperaments, and provides a flexible way of modeling their influence on the behavior of synthetic autonomous agents. Picard, R. (2001) proposes that machine intelligence needs to include emotional intelligence and demonstrates results toward the goal: developing a machine’s ability to recognize the human affective state given four physiological signals and compares multiple algorithms for feature-based recognition of emotional state from this data. TAME is framework for affective robotic behavior that deals with an exploratory experimental study to identify relevant affective phenomena to include in the framework in order to increase ease and pleasantness of human-robot interaction. Moshkina, L.(2005), Moshkina, L.(2003) and Arkin, R. (2005) We present the affective state model in the three dimensional space and then describe a systematic method to demonstrate the changes in affective states of the agent. That comprehensive model provides a complete platform for the emotional state of the character by considering all mixed emotions. Furthermore, a realistic transition system is proposed to control changes in internal affective states which have not been investigated in previous emotional models. Tension and energy are believed as two principle parameters for representing the mood of human being in psychological studies. Russell, J.(1980), Thayer, R. (1989) and Thayer, R. (1996). We have considered these two dimensions as Activation (act) and Motivation (mot) axes in the 2D affective state plane in order to categorize affects in a methodical manner. Fundamental affective states can be considered as a fragment which is represented by an area in the affective state coordinate system. It should be noticed that these areas might have overlaps and could not be separated as pure states. Each of the fundamental affective states fragments can be segmented into more detailed affects. page 4 Emotional Intelligence Engine for Serious Game Mentioned affective state areas can be modeled by bean-shaped curves of genus zero with a single singularity with an ordinary triple point at the origin to illustrate their coverage in the affective coordinate system: (x2+y2)2=x3+y3+a(x2+x−y) (1) which gives a crooked egg curve when a is zero, and a bean curve when a is one. State areas can be modeled by transecting the origin for plotting the above closed curve as Xi=xi−xact , Yi=yi−ymot in the Activation-Motivation plane. So any affective i i states can be identified according to its transferred origin as equation 2. Os=  Xi=xi−xactiYi=yi−ymotiZi;j=zSubi;jwhere    1≤i≤25 1≤j≤10 (2) where i represents state and j shows the sub-state number. By having the position of the origins, Equation 1 could utilize in order to specify affective state areas. Any of the main affective states include several sub-states which represent more details internal state. Sub-states can be represented as the third dimension of the affective state coordinate. During interaction between two agents in serious game platform different emotions could be transferred amongst them. Such emotional transit can be modeled by considering each emotion as combination of 6 basic emotional values over time: happiness, sadness, disgust, surprise, anger, and fear. Furthermore, the agent itself is in one of the internal states over time. The affective state of the agent at any time depends on its initial state plus the interaction result caused by the interaction input and that result should be affected by the extra parameters which push the resulted state toward the certain point in the affective space. To model the system to link interaction and affective state, the transition in the affective state space has formulated as following: St Act−Mot−Sub = St−h+ηΦ+βΓΔ (3) where St is the affective state of the agent in the affective space at time t and St-h is affective state in time t-h, where h is the processing time gap as discrete system; F is the vector field over the states which converges to the certain point in affective state coordinate system. F can be considered as the gravitational field of a point mass due to a point mass c located at point P0 having position r0 as: Φ= −kc ( r− r0) | r− r0| (4) c>0 is a constant, Φ points toward the point r0 and has magnitude|Φ|= kc | r− r0|2 ; page 5 Emotional Intelligence Engine for Serious Game η is the adjusting parameter for converge vector field; β is the affective state coefficient which represents the personality of the agent that controls the rate of change in the mood. Larger β means that the state would change faster which makes the agent more moody; Γ is the learning rate. The change in state is different when agent has more interaction and this parameter helps to have more realistic changes in affective state; Δ = ΔAct−Mot−Sub is the 3D normal vector to transfers the state over time in affective state space based on the emotional input according to the interactions. First two components are in the Activation - Motivation plane which are driven from emotional input: 6 ΔAct−Mot= 6 ∑ eMot Mot(i)+ ∑ eAct Act(j) m=1 m m=1 m (5) where Mot and Act are Motivation and Activation axes and eis are 6 values of happiness, sadness, disgust, surprise, anger, and fear in Activation and Motivation directions. The third component of Δ represents the movement in sub-state direction which obtained from the rate of the first two components: ΔSub=| d Δ |(k) dt Act−Mot (6) In this way the vector kΓΔ finds its direction to reach the next affective state. Artificial Endocrine module Natural endocrine system is viewed as a network of glands that works with the nervous system to secrete hormones directly into the blood so as to control the activity of internal organs and coordinate the long range response to external stimuli. Purves, W. (1983) Hormones which are chemicals released by components of the endocrine system affect other parts of the body and play a significant role in the endocrine system so as to preserve homeostasis. Here, we will introduce the relation of hormones with human emotion and behavior which can be implemented into the emotional layer of the agent in the game. Virtual biological systems considered as research field of biological inspired computing. Artificial Neural Network (ANN) is one of the well known tools in computational intelligence techniques. In the same way, the endocrine system could also be a very useful tool, but there was not any interest in that apart from some basic systems like Timmis, J. (2004) and Vargas, P. (2005). This paper focuses on the hormones which are related to emotions Morrison, M. (2000) and Pfaff, D. (2004) and biological qualities Norman, A. (1997). For emotionrelated hormones, we consider four hormones namely Dopamine, Serotonin, Endorphin and Oxytocin, The level of these hormones is related to the emotional situation of the human being as it is presented in Table 1. page 6 Emotional Intelligence Engine for Serious Game Table 1: Emotional Hormones Furthermore, the affective state and behavior of the agent should be affected by the physiological parameters, such as blood pressure, blood glucose and heart rate. These parameters are influenced by hormones as well. Hence, we introduce a group of hormones which are closely related to the physiological parameters of humans. The effect of these 8 biological hormones is explained in more details in Table 2. Table 2: Biological Hormones Proposed artificial endocrine system can be implanted into the affection system of the agents in the game, so that their affective states and behavior would be affected by a variation of hormone levels. Based on the internal states of agent and external stimulations, the agent will signal the glands to generate the required amount of hormones. Hence, the agent will experience change in the emotional state and biological need. In our system all hormones are considered to be secreted by two parameters: • The activation function which can be presented by employing the logistic function and page 7 Emotional Intelligence Engine for Serious Game • The gland bustle that should be considered through all the stimuli channels. So the glands secretion can be modelled as equation 7: m×n 1 Λq = ∑ ρiΘq 1+exp(−aq) q=1 (7) Above representing shows that the gland secretion, Λ, is the product of the each gland bustle, Θq, by considering ρq as the stimuli weight, which can be activated 1 . The gland bustle should be 1+exp(−aq) considered over m emotional values. n is number of different agents in the game environment that considered agent has interaction with them. The coefficient a in the activation function depends on the current volume of the hormone in the system. In this way, each agent in the game environment can be equipped with basic emotional hormones to control affective situations like being happy, talkative and energetic; also it will have basic biological needs like feeling hungry, sleepy and sick. That capability makes it possible to see dynamic and realistic behavior from the agent. through the nonlinear activation function Experimental Results We have developed a simulator and applied the mentioned theories to that virtual environment. We designed the emotional layer for that system. The overall platform of this simulator is presented in Figure 1. Figure 1: The Game simulator environment At the beginning of the game, 10 agents are placed in random positions in the environment. For each of them, random interaction area is considered as well. Agents generate random emotional values in their interaction area. The user can start the game by navigating through the environment using mouse clicks. When page 8 Emotional Intelligence Engine for Serious Game interaction areas of two agents meet, the emotional values are transferring between those agents and according to mentioned theories, the affective state of each one changes. The levels of hormones also change by time base on situations and interaction. We focus on affective properties of the character in the game and introduce the systematic method in order to generate such affection. The affective state system also developed as it has described in the previous sections. Figure 2 presents the affective state space which includes affective sates that are generated according to the equation 1, by using bean shape closed curves. Each of affective sub-states are demonstrated by volume in the 3D space and, as illustrated, several affective states overlap and share the subspace in the affective system which demonstrates the mixture of the emotional states. The state of the agent can be considered as one point inside this space. If the position of that point confined inside any of these volumes, then the internal state of the agent belongs to that category of affective state. Figure 2: Affective states are modeled as volumes in the 3D affective coordinate system The game is programmed in Matlab R2008b environment as it gives lots of possibilities for modeling using mathematical formulas. Conclusion We have introduced an intelligent emotional layer for serious games in this paper. We believe that such a module can be employed in several serious game platforms in order to benefit learning aspects. A comprehensive emotional model which considers affective state has been presented for the agent. We also presented the system for an artificial endocrine system in order to improve the affective power of the agent. By implementing such emotional agent in a simulator, we have developed the system with realistic emotional behaviors. We believe that such platform could be added to several games in order to improve realistic behavior. page 9 Emotional Intelligence Engine for Serious Game Author information Hooman Aghaebrahimi Samani is a PhD scholar of NUS Graduate School of Integrative Science and Engineering, pursuing his postgraduate degree at Mixed Reality Lab in the Department of Electrical and Computer Engineering, National University of Singapore. Before commencing of his PhD in Singapore, he studied different fields of Robotics in UK, Netherlands, Germany and Iran. He has participated in several Robocup competitions and won world prizes in this regard. Robotics and specifically Artificial Intelligence is his main research interest. Doros Polydorou is a PhD scholar of Brunel University, UK, currently on internship in NUS Graduate School of Integrative Science and Engineering. His background is an MSc in Computer Animation and a BSc in Multimedia Design and Technology, both from the University of Kent, UK. He is currently in his final year of PhD concentrating on how game design theories and methodologies can be applied to other disciplines, especially art installations and performance. Tim Marsh is an Assistant Professor in the Communications and New Media Department, Faculty of Arts and Social Sciences at the National University of Singapore (NUS). He is also a faculty member in the Mixed Reality Lab (MXR) and the Keio-NUS CUTE Centre, Interactive and Digital Media Institute (IDMI). Before joining NUS he worked in the Integrated Media Systems Center (IMSC) and InfoLab at the University of Southern California (USC), Los Angeles, CA, USA. He has a Ph.D in Computer Science specializing in Human-Computer Interaction (HCI) from the HCI Group, Department of Computer Science, University of York, UK. At NUS, he teaches graduate modules in Serious Games and Learning Media, and Human-Computer Interaction. His research interests are in design, development and analysis of serious, computer and experimental games, and virtual and mixed reality environments. Over the last ten years he has worked on various virtual and game-based learning research projects funded by the UK EPSRC and US NSF and he is currently principle investigator (PI) on a two-year Singapore-MIT GAMBIT Games Lab research project on serious games funded by the Media Development Authority (MDA). Adrian David Cheok is Director of the Mixed Reality Lab, National University of Singapore. He is Associate Professor in the Department of Electrical and Computer Engineering. He became Full Professor in Keio University, Graduate School of Media Design from April 2008. He is Editor/Associate Editor of the following academic journals: Transactions on Edutainment (Springer), ACM Computers in Entertainment, Advances in Human Computer Interaction, International Journal of Arts and Technology (IJART), Journal of Recent Patents on Computer Science, The Open Electrical and Electronic Engineering Journal, International Journal of Entertainment Technology and Management (IJEntTM), Virtual Reality (Springer-Verlag), International Journal of Virtual Reality, and The Journal of Virtual Reality and Broadcasting. Adrian David Cheok, who was born and raised in Adelaide Australia, graduated from the University of Adelaide with a Bachelor of Engineering (Electrical and Electronic) with First Class Honors in 1992 and an Engineering PhD in 1998. page 10 Emotional Intelligence Engine for Serious Game List of references Marriage(2006). The Marriage, Hamble, R., Online: http://www.rodvik.com/rodgames/marriage.html Sims(2000). The Sims. Electronic Arts. 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