Agents with Emotional Intelligence for Storytelling

Transcription

1 Agents with Emotional Intelligence for Storytelling João Dias and Ana Paiva INESC-ID and Instituto Superior Técnico, Technical University of Lisbon, Tagus Park, Av. Prof. Cavaco Silva, Porto Salvo, Portugal Abstract. One core aspect of engaging narratives is the existence and development of social relations between the characters. However, creating agents for interactive storytelling and making them to be perceived as a close friend or a hated enemy by an user is an hard task. This paper addresses the problem of creating autonomous agents capable of establishing social relations with others in an interactive narrative. We present an innovative approach by looking at emotional intelligence and in particular to the skills of understanding and regulating emotions in others. To that end we propose a model for an agent architecture that has an explicit model of Social Relations and a Theory of Mind about others, and is able to plan about emotions of others and perform interpersonal emotion regulation in order to dynamically create relations with others. Some sample scenario are presented in order to illustrate the type of behaviour achieved by the model and the creation of social relations. 1 Introduction People usually find emotional stories remarkably interesting [6], and this has been explored in narrative since its earliest days. Aristotle identified emotions as a core feature of classic tragedy: A perfect tragedy should (...) imitate actions which excite pity and fear, this being the distinctive mark of tragic imitation [2]. He also defined that the most relevant constituents in the tragedy or drama are the plot and the characters. Thus, aiming at creating interactive storytelling systems, researchers in the autonomous agents community started by trying to create autonomous synthetic characters [18]. Their first problem was to make virtual non-real characters evoke emotions in viewers like human actors do in a dramatization, and appear to be alive in eyes of the viewers. Bates named this concept as believability[4]. However, with the goal of building increasingly engaging storytelling systems, one also needs to address the plot of the narrative. One core aspect of dramatic plots, which is capable of eliciting even stronger emotions is the existence and development of social relations between the characters. Ryan [20] states that dramatic narratives focus on evolving networks of human relations, and most of the actions in their plot correspond to either verbal communication or to physical actions that affect interpersonal relations. S. D Mello et al. (Eds.): ACII 2011, Part I, LNCS 6974, pp , Springer-Verlag Berlin Heidelberg 2011

2 78 J. Dias and A. Paiva Establishing social relations is thus critical if we want the user to interact with an agent in an interactive narrative, or role-playing game, where the relation between the two takes a preponderant role in the plot. Although one can build agents that are considered believable, making them to be perceived as a close friend or a hated enemy by an user, is certainly a more complex task. In order to do so, we need to explore mechanisms that mimic the way that relations between real people evolve. With this in mind, this paper addresses the following question: How to create autonomous agents capable of dynamically creating social relations with others in an interactive narrative? The problem of creating social relations in autonomous agents has already been addressed in many different ways. Works such as of Prada [16] and Psychsim[17] are important landmarks. However, in here we present an innovative approach that draws inspiration from one particular type of social intelligence: emotional intelligence. 1.1 Emotional Intelligence According to Salovey and Mayer [21], Emotional intelligence refers to an ability to recognize the meanings of emotion and their relationships, and to reason and problem-solve on the basis of them. In other words, it corresponds to the understanding of the impact that emotions have on the self and on others, how emotions are created, and also being able to use this knowledge to regulate emotions on the self and in others. Continuing their research in Emotional Intelligence, Mayer and Salovey [9] put forward a four branch model that divides Emotional Intelligence in four main skills: perceiving emotions in oneself and others - has to do with the perception and expression of emotion through gestures, facial expressions, or other communication mechanisms. using emotions to facilitate thought - the second skill, focuses on using emotions to guide cognitive processes, such as learning and adaptation, attention and decision making. understanding emotions - the idea here is that emotions convey information. For instance, Anger indicates a possible intention of harming other. Therefore, understanding emotions involves understanding the meaning of emotions, together with the capacity to reason about those meanings. managing emotions - once a person understand emotions, it can manage one s own and other s emotions in order to promote social goals. For instance, one can go to see a movie when distressed in order to feel better, or do something pleasant to help a friend to overcome some bad mood. Given the relevance of the fourth skill, there is an entire area of research in psychology that deals with the concept of managing emotions. Some psychologists refer it as Emotion Regulation, and we will use this same term throughout this

3 Agents with Emotional Intelligence for Storytelling 79 document. The construct of Emotion Regulation first appeared in the developmental psychology literature [22], and then in the adult literature. According to Gross [7], Emotion Regulation refers to the heterogeneous set of processes by which emotions themselves are regulated. These processes may dampen, intensify, maintain the intensity or even change the type of both positive and negative emotions. They may be automatic/unconscious or controlled/conscious. These processes may be intrinsic, when they involve regulating emotions in self (also called self-regulation), or extrinsic when they involve regulating emotions in others (also called interpersonal emotion regulation). But how is emotional intelligence related to the development of social relations? Several studies [13,12] have shown that people who have higher emotional intelligence have more positive social interactions with peers. An additional study with german students also indicated that students with better scores in managing emotions in others were more liked and valued by the opposite sex[13]. Thus, it seems that the high emotionally intelligent individual is rather successful at establishing social relations with others. Therefore, if we are able to understand how regulating emotions in others affects relations, and if we are able to model this capability we can hope to have better results at dynamically establishing relations with others. 2 Related Work The problem of creating social relations in autonomous agents has already been addressed in many different ways. For instance Psychsim [17] is an environment for multi-agent simulation that employs a formal decision-theoretic approach using recursive models. This allows the agents to reason about the behavior and beliefs of other agents and to use communication to influence other agents beliefs about agents. Psychsim was used to model social-simulation scenarios that represent concepts such as social power, support, consistency and affinity. However, consistency and affinity are mainly used to help decide if the agent trusts the messages other agents send him and Psychsim does not model emotions and its influence on social relations. Avatar arena[19] presents an agent model that can dynamically change social relationships between agents. The model is based on theories of cognitive consistency, such as Heider s Balance Theory [8], and is used for multi-party negotiation dialogues. Following Balance Theory, when an agent discovers a mismatch between his opinion about a concept and another agent s opinion about the same concept (e.g. he likes to talk about soccer and someone else does not), this causes the experience of a dissonance and triggers a change in the social distance to the other agent so that a balance is achieved again. Existing social relations, affective state and personality are then used to generate the dialog and negotiation between the agents. Nonetheless, the focus in Avatar arena is in the use of relations to directly influence the behaviour of the agents and it does not model behaviour that aims at explicitly changing those relations.

4 80 J. Dias and A. Paiva The SGD Model proposed by Prada [16] focus on modeling group dynamics between social agents. Amongst other things, it models relations of social power and of social attraction which are used to help build the dynamic of the group and resulting group behaviour. SGD divides agent interactions in two main categories: instrumental and socio-emotional. Instrumental interactions are task related (e.g. solving a cooperative problem) while socio-emotional are used to change the existing relations within a group. Similarly to Avatar arena, SGD model uses Heiders balance theory to determine that when an agent observes a positive socio-emotional interaction towards him, then his attraction for the performer of the interaction will increase. Emotion regulation only got the attention of the autonomous agents research community very recently. For that reason, computational models of reasoning about emotions, and emotion regulation are at the moment (to our knowledge) just a few. One of them is Adam s Work. Adam [1] presents a very complete formalization of a cognitive logical model of emotions, which allows us to reason about, plan for and even explain an agent s emotions. Quite relevant to our work, is Adam s proposal for a formalization of coping behaviour, which can be used to regulate emotions in others. However, there is no explicit model for social relations (social behaviour is implicit in the rules defined), and the model does not consider strategies to worsen affect. Boss and Lange [5] aim at developing IVAs with a Theory of Emotion Regulation (ToER), i.e, a Theory of Mind that models emotional states of others, and which allows them to reason about other s emotion regulation processes. Although the ToER model uses explicit interpersonal emotion regulation strategies,it was not designed with the goal of exploring the effect of emotion regulation in social relations. Thus, it also does not model social relations and explicit social goals. The ToER model shows us that in order to regulate emotions in others, we must build a theory of mind of others, which includes their emotional state. 3 Conceptual Model In order to explain the proposed model we need to briefly overview the FAtiMA- PSI architecture as it was implemented on top of it[10]. This architecture was chosen because it provides a set of features necessary to address our research problem, such as the capability of generating and modeling emotions. FAtiMA-PSI FAtiMA-PSI follows OCC Theory of emotions [15] where emotions are seen as valenced (good or bad) reactions to events. Whenever an external event or action is perceived, both the knowledge base and autobiographic memory are updated. At the same time, the agent s motivational state is updated. For instance, if the agent finished an eating action, its need for energy will go down. FAtiMA- PSI models five drives: Energy, Integrity, Affiliation, Competence and Certainty. Whenever the motivational state is updated, the change on the drives is used to determine the desirability of an event. The determined desirability is used together with other appraisal variables to generate a wide range of emotions

5 Agents with Emotional Intelligence for Storytelling 81 from Joy and Distress to Pity and Anger. The generated emotions are also stored in memory and associated to the events that triggered them. After the appraisal phase, both reactive and deliberative components perform practical reasoning. The reactive layer uses simple and fast action rules that trigger action tendencies. Action tendencies define a character s reactions to particular emotional states (e.g. crying when very distressed). In addition, when the event is appraised, the deliberative level checks if any goal has become active, and determines the goal s utility based on its current drives. The utility of a goal depends on the impact of the goal on the drives and also on the current level of the drive 1. The agent then tries to build a plan to achieve the most relevant goal. This is done using a continuous planner [3] that can be extended to incorporate planning about emotions. Architecture for Emotionally Intelligent Agents The aim of the model is to dynamically create believable social relations by endowing the agents with the capability of reasoning about and regulating emotions in others. Figure 1 shows the extensions to FAtiMA-PSI in order to do this. First of all, we need an explicit model of social relations, which is implemented by the social relations component. Furthermore, the appraisal process was divided into two separate processes, according to [14]. The first one, appraisal derivation, is responsible for evaluating the relevance of the event according to the agent s needs and determine a set of appraisal variables (e.g. desirability, desirability for others). The second process, affect derivation, takes the appraisal variables as input and generates the resulting emotions according to OCC Theory of emotions. This division facilitates the process of reasoning about emotions. Having the capability of performing interpersonal emotion regulation requires us to model not only internal processes of the agent but also the processes of others. Similarly to Boss s and Psychsim s approach, the agent will need to create a model of other s emotional state, social relations, appraisal rules and action tendencies. These will constitute the Theory of Mind (ToM) component. Finally, the planner will then represent and reason about the emotional processes in order to achieve a set of high-level social goals, thus generating the agent s social behaviour. Social Relations Component The dynamics of social relations (which defines how social relations change with time) follows Heider s Balance theory [8]. Balance Theory hypothesis is that people avoid unstable cognitive configurations and that they mobilize their efforts to resolve it and change it to a stable state. For instance, if someone likes something you dislike, you tend to either start liking that thing or liking less the person. This principle is applied to the perception of events, similarly to SGD model. Suppose that the user (or another agent) has performed an action undesirable for the agent, such as insulting him. If the event was performed by 1 To know more about how the goal utility is determined and how the drives are updated, please refer to [11].

6 82 J. Dias and A. Paiva Fig. 1. Proposed Model for Agents with Interpersonal Emotion Regulation Skills the user, then we assume that the user intended to perform that event and as such, the event is desirable to the user 2. This corresponds to an unstable state. However, differently from SGD model, events are not pre-classified as positive or negative. Instead, we use the result of the appraisal to determine whether a perceived event is positive or negative. Thus, whenever a new emotion caused by another agent is added to the emotional state, the social update component will analyze the valence and intensity of the emotion and update the corresponding relation proportionally in order to converge to a more stable state. Thus, positive emotions increase the interpersonal relation, while negative emotions decrease it. Stronger emotions cause bigger changes in the relationship. Theory of Mind Component The ToM component is responsible for storing and updating the knowledge that the agent has about other agents. It creates one ToM model for each existing agent that he perceives. So, if there are two other agents A and B, thetom component creates a ToM Model for A that will store the knowledge that the agent has about A and another independent ToM Model for B. The ToM includes information about others beliefs, memories, needs, emotions and social relations. When an event is perceived, the ToM component determines if others have perceived the same event (for instance, looking at which agents are near it). If so, it uses the event to simulate how others perceive, remember and appraise the event. Then, the result of each simulated processes is used to update the all the internal structures of the corresponding agent model (e.g. affective state, and social relations). Reasoning and Planning about Emotions In the proposed model, the planner was extended in order to be able to build plans of actions that aim at establishing emotions in others (or in self). With that aim, two special Meta-Operators were created to represent the processes of appraisal derivation and affect derivation (shown in Figure 2). Unlike normal operators, these operators have some semantics attached and may generate several instantiated operators. 2 This is a simplification, since the user may have done the action unintentionally or may have been coerced to do so.

7 Agents with Emotional Intelligence for Storytelling 83 Fig. 2. Mapping Emotional Processes into special STRIPS Operators Whenever a Meta-Operator is instantiated in order to achieve a precondition, it will use an internal function to generate the corresponding values for its own preconditions depending on its effects. For instance, if the goal is to make agent B Joyfull with intensity 3, the Meta-Operator AffectDerivation will be instantiated with [Agent]/B, [Em]/Joy, [X]/3 and it will apply the inverse function of the affect derivation function used in FAtiMA-PSI, in order to determine the set and values of appraisal variables that could make agent B experience a Joy emotion with at least intensity 3. In this example, the operator generated would have the precondition Appraisal(B,Desirability,3). This process of instantiation may possibly generate more than one operator. Following the previous example, when applied to the plan, the Meta-Operator AppraisalDerivation will try to find actions that are considered desirable with at least value 3 by agent B. This is done by simulating the effect that all possible actions can have in agent B drive s and determining the corresponding desirability. If more than one action satisfies this condition, several instantiations of the AppraisalDerivation operator will be generated (and added to different alternative plans), representing the alternative ways of achieving the desired appraisal variable. Social Goals The previously described components are used to define a set of high-level social goals which are responsible for the agent s social behaviour. IncreaseRelation - The IncreaseRelation goal becomes available for an agent A when he thinks that another agent B likes him less than he likes B. Following Balance Theory, the rationale is that agent A will try to make the relationship more consistent by increasing B s relation towards him. For this reason, the goal s success condition is for A to cause a positive emotion in B. DecreaseRelation - Becomes active when an agent A does not like another agent B, but thinks that B does like him. Similarly to the increase relation goal, this goal tries to create more consistent relations but this time by lowering B s relation toward A. To that end, the agent will try to cause a negative emotion in B. RegulateNegativeEmotion - The RegulateNegativeEmotion goal is activated when the agent thinks or perceives that another agent, which he likes, feels a negative emotion. The success condition of the goal is to make the target agent feel a positive emotion.

8 84 J. Dias and A. Paiva Once the goals become active, it does not necessarily mean that they will be selected and executed. Social goals are closely connected with the agent s need for affiliation (meaning that the stronger the need, the stronger the goals importance). And a goal will only be selected for execution if it surpasses a predefined relevance. As such, the agent will perform social interactions more frequently and share emotions if its need for affiliation is high. 4 Scenarios Two simple scenarios were created in order to show the development of social relations and illustrate the type of social behaviour that can be achieved by the model. In the scenario, the agents have a set of basic actions and goals, such as easting and resting, that can be executed to achieve the agents needs: energy, integrity and affiliation. Additionally, the social goals defined above were added to the agents and a set of actions that influence other s needs were defined. Kicking others lowers their integrity, insulting or gloating has a negative impact on the target s affiliation and stealing food provides a way of influencing other s energy. As for positive actions, complimenting or hugging others increases their affiliation, healing improves integrity and giving food has an impact on energy. Three agents were created with distinct initial social relations. Agent Luke likes John but dislikes Paul; John likes the other two agents; Paul dislikes Luke but is neutral to John. All of them are neutral to the user. Figure 3(a) shows an extract of the text-based result of the simulation, with the actions performed by the agents. One can see the different types of interaction according to the existing relations. Agent Luke does nice actions towards John (giving food) and bad actions towards Paul (insulting and taking food away). (a) Social interactions between agents (b) Luke interacts with the user Fig. 3. Text-based simulations of two simple illustrative scenarios

9 Agents with Emotional Intelligence for Storytelling 85 It is important to point out the evolving dynamics of the behaviour presented. When performing interpersonal emotion regulation, the agent will use its model of appraisal to dynamically determine what are the actions that would elicit the desired emotion according to the target s current needs. Furthermore, the user s interaction can make the relation with the agent s evolve, thus changing their behaviour towards the user. As highlighted in Fig. 3(b), when the user kicks Luke, it will change its relation from neutral to negative, making the agent perform socially negative actions towards the user. 5 Summary and Future Work This paper addresses the problem of endowing autonomous agents with the capability of dynamically creating social relations with others. To that end, we explore emotional intelligence skills and in particular the capability of regulating emotions in others. The agent architecture proposed has an explicit model of the existing socio-affective relations between agents and how they evolve according to experienced emotions, which follows Heider s balance theory. In order to perform interpersonal emotion regulation, the deliberative component models a set of meta-operators that represent the emotional processes. Using such operators, the planner is able to build a plan of actions to achieve a desired emotional state in others. A set of high-level social goals, which depend on existing affective relations, are then used to generate explicit social behaviour such as wanting to increase an agent s relation toward another. We presented two small scenario that aim at illustrating the type of behaviour obtained with the very general social goals. We believe that the proposed model offers an flexible and general mechanism that can be used to try to establish social relations with others. We plan to create a more elaborate scenario where the user gets to play with several agents (with distinct relations) to complete a given quest. We will then assess if the proposed model can make the existing social relations more easily perceived by the user, and if the agents are able to establish stronger relations with the user. Acknowledgements. This work was partially supported by European Community (EC) andby a scholarship (SFRH BD/19481/2004) grantedby the Fundação para a Ciência e a Tecnologia (FCT). It is currently funded by the EU FP7 ICT project LIREC (LIving with Robots and IntEractive Companions). The authors are solely responsible for the content of this publication. It does not represent the opinion of the EC or the FCT, which are not responsible for any use that might be made of data appearing therein. References 1. Adam, C.: Emotions: from psychological theories to logical formalization and implementation in a BDI agent. Phd thesis, Institut National Polytechnique de Toulouse (2007) 2. Aristotle: Poetics. Penguin, London (1996)

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