Interactive Social Agents from Deep Data

Interactive Social Agents from Deep Data Joana Campos and Ana Paiva INESC-ID and Instituto Superior T´ecnico - Universidade de Lisboa, Av. Prof. Cavaco Silva, Taguspark 2744-016, Porto Salvo, Portugal joana.campos@ist.utl.pt ana.paiva@inesc-id.pt

Abstract. The multidisciplinary challenge of modelling agents have been driven by theory explaining social phenomena. Yet, these generic models lack of expressiveness. For that reason, data-driven approaches to the design of agents have been pursued, mainly for modelling non-verbal behaviour. In this paper we argue that real data is not only useful for that modality, but it can also assist agent’s design in different phases of the process at different levels of granularity. Furthermore, deep data, which inform us about user’s perception, emotions and motivations is valuable to build fluid interactions with virtual humans. We illustrate our stance with two case studies where we study interpersonal conflict. One study describes the design of agents to populate a serious game aimed at teaching conflict resolution skills to children and the other describes an experiment designed to extract deep data from a dyadic interaction prone to conflict emergence.

Key words: Virtual Agents, Design, Interpersonal Conflict, Data-driven

1

Introduction

Intelligent Virtual Agents (IVAs) are becoming commonplace given their wide application in several areas such as healthcare, education, simulation and games.

Hence, more often, agents have to collaborate with humans, compete or even to act under a specific role in increasingly dynamic environments. To perform all those tasks effectively, in a way that suits human standards, such agent systems should be able to decode other’s social signals, produce non-verbal behaviours, generate and manage dialogue, plan and decide, which involves real-time reasoning and action. Inevitably, designing agents that are socially aware of their interactional partner can be a bewilderingly complex task.

Over the years, researchers have tackled the challenge of modelling and expressing each of the aforementioned natural human modalities, by integrating the knowledge and methodologies from computer science with concepts from sociology, psychology or linguistics, to enumerate a few examples. Frequently, the development of these computational models is driven by theoretical concepts, which provides a systematic way to tackle a problem, towards a generic representation of social phenomena. Although it is an utterly valid approach,

2

Joana Campos, Ana Paiva

researchers are confronted with several difficulties. First and foremost, the integration of general theoretical concepts present in the social sciences literature into a program, requires interpretation and formulation of assumptions from the development side. Additionally, it is often hard to use the computational model to represent very concrete cases, since the models are not expressive enough without some authoring tweaks. This is mostly due to the lack of empirical data supporting the formulated theories, which would allow us not only to justify authoring decisions, but also help us to better evaluate the behaviours generated by our computational models.

In a nutshell, theoretical research tries to explain the macro-concepts and offers a simplification of a complex phenomena, but it often disregards the specification of those concepts into small details, which would help to derive concrete instantiations for the agents’ behaviour. Hence, unsurprisingly, data-driven approaches have become increasingly popular, because it allows the validation of design choices empirically [16]. This type of approach may help researchers to achieve a better balance between simplification and realistic behaviour, by focusing on the expressivity of the model. Yet, data-driven approaches start by collecting enormous amounts of data for finding representativeness of types of behaviour. First, it can be a very daunting task to gather large amounts of data.

Then, the more data we get, the more data has to be annotated and there is no limit for the number of annotation layers, which depends on the granularity of behaviour one is seeking [16]. But regardless of these open research issues, this methodology for designing intelligent agents enables the generation of models that augment the agent’s interactive capabilities, mainly because it allows researchers to focus on small units of the interaction under the assumption that people’s interactions are driven by very subtle cues. This cues are the scaffold for fluid interactions, which is the uttermost goal in virtual agent’s research.

According to this view, Morency et al. [13] focused on multi-modal behaviours and its predictive power. Their aim was to understand how humans use the backchannel feedback (e.g. head-nods, paraverbals) to interact with other humans and then generate dyadic conversational behaviour. Another example is the work by Endrass et al. [6], who analysed a multi-modal corpura to investigate culture-specific interpersonal communication management. Her (theory-based) model of interpersonal communication was then continuously refined with statistical data extracted from a video corpus of German and Japanese first-time meetings [7].

Real data is not only useful for modelling non-verbal behaviours. Real and deep data can feed the agent’s design process in several ways, depending on the context of application and the level of granularity required for a certain type of task. Nevertheless, theory from diverse areas of research should not be discarded and an hybrid approach to the design of social agents is probably more adequate.

In this paper, we present two case studies in which interpersonal conflict was explored with children. Our aim is to expose how different levels of granularity of data and theory combined, can help us to express the phenomenon in two different contexts. Furthermore, we aim to highlight the importance of gathering

Interactive Social Agents rooted in Deep Data

3

deep data from potential users of a system (independently of its application).

By deep data we mean, rich data representative of one’s perspectives, emotions and motivations. We believe that these elements establish the context for many dyadic interactions, which are no more than a form of relating.

2

The Case of Interpersonal Conflict

In our work, we aim to explore how subtle forms of conflict evolve in social interactions, for a better representation of the phenomenon in dyadic interactions. A simplistic view of conflict is predominant in the Multi-Agent Systems’s literature, where the phenomena is commonly addressed as a failure or synchronisation problem [15]. But conflict is more than that, it is a form of relating [4], in which emotions play a relevant role. Therefore, we started by articulating insights from the social sciences literature towards a more natural representation of conflict in agents’ behavioural systems [3]. Our position is that conflict is a dynamic process and transitions between states are driven by an agent’s emotions, which are responsible for activating or deactivating it in a conflict loop.

Yet, despite the massive research on conflict in the social sciences, there is still some uncertainty on how to translate the theory to more specific parameters that together provide an adequate description of the phenomenon. Conflict happens at different levels of the social interaction and it is not clear what actually happens during this multi-level process.

To learn more about the conflict phenomena we turned to humans, in particular children, to understand how the process unrolls. We explored conflict in two different contexts and hence we followed two different methodologies to gather data. In one study (Case Study 1 in section 3) in , we were interested in the high-level dynamics of the conflict phenomenon to represent it in a game intended for teaching conflict resolution skills to children [2]. The game was populated by virtual agents and we claim that the agents have to depend on emotional reactions to accurately respond to one’s expectations of natural conflict scenarios. Along these lines, the agents have to be created based on real data gathered from real people (children) without shortcomings for defining their behaviours. Therefore, to collect children’s perspective on the subject we employed an adaptation of cultural probes described in the next section.

Following this, to get more detail, we were interested in dyadic factors of interpersonal conflict and how conflict can be modelled from an agent’s perspective (Case Study 2 in section 4). Conflict requires interaction and emotions that regulate it. Thus, we created an experimental setting (described in section 4) that reduces real-life to a mixed motive game, in which children’s previous experiences and relation with the interactional partner play a relevant role in the interaction. Our aim is to understand how subtle forms of conflict unroll, by analysing micro-level behaviours and establish a link to high-level psychological constructs.

4

3

Joana Campos, Ana Paiva

Case Study 1: Capturing High-Level Deep Data

When the systems are task specific or, as it is the case, have specific learning objectives is essential to adopt a methodology that iteratively involves the final user. Thus, in this work, to glimpse children’s emotional worlds and to assess whether or not and under which conditions conflicts exist, we centered the user in the design process. As part of this process, we used an adaptation of cultural probes — Conflict Probes —, which aim at gaining insight of realistic game mechanics and narratives, by closely observing how children behave at school.

Taking advantage of cultural probes’ characteristics , we expected candid responses from children that would guide us from initiating actions of conflict episodes to climax and finally the resolution strategies that they employed.

Conflict probe is a variation of the original package introduced by Gaver [8].

Despite being an adaptation it was designed to embed the main characteristics of this method. The probe study was designed to be user-centred, to allow selfdocumentation and also has an exploratory character [9]).

3.1

Aim

The purpose of this study was to understand the users’ social worlds, in terms of the ways in which children behave at school. By using cultural probes, we expected to gain insight into these practices without being too close and interfering in the ways that children interact with each other. The aim of our probe pack is to collect sensitive information about children’s individual feelings and behaviours in situations that they identify as important to them. In that sense, we expected that children would show us how they themselves appraise their social environment, encouraging us, as designers, to step back from our preconceived notions of their reality, and to identify novel and surprising aspects of children’s lives.

3.2

Adaptation

The probe package was created in order to be suitable to this new context. First, we had to adapt the probes to the theme, to the environment in which they would be employed and we had to create the specific materials for this study. Second, we would be working with children. For that reason, the tasks had to be adapted in terms of ambiguity and provocative attitude.

3.3

Research Materials

The conflict probes (Figure 1) comprised a pack of 7 envelopes, each one with a written task inside, and a set of materials that children could use to perform the activities. In addition, a calendar (see Figure 2 below) was used as an anchor point to help children identify which envelope to open and when.

The tasks were designed in such a way as to create an evolving interaction with the user by increasing the level of ambiguity as the participant explores the

Interactive Social Agents rooted in Deep Data

5

Fig. 1. Probe pack

Fig. 2. Calendar to keep track of tasks.

delivered package. As they progress through the tasks, the children are able to build on the concepts apprehended in previous tasks. A description of each of the tasks is provided in the following section.

3.4

Participants

The cultural probe study focused on 51 children with ages between 9 and 12 years old. Conflict probes had to be completed in class during a five 5- week period.

Each week teachers made available a 45-minute slot to the activity. Teachers suggested such setting, because at home children would be influenced by their parents. Teachers were asked not to intervene and every child had to complete the activities on their own.

3.5

Probe’s Tasks: Breaking down ”conflict” into pieces

When asked about conflict, children’s personal definition does not go much further than the word ”fight” [12]. For that reason, dividing the concept into pieces seemed to be essential in order to extract meaningful information from the children and at the same time make easier to them to talk about the subject.

The probe pack was composed by 7 self-contained tasks designed around five dimensions of conflict: participants, causes, strategies, resolutions and outcomes.

Each one of the tasks (if not all) would fit in each one these dimensions would be dependent of its returns.

At the same time, as an attempt to engage the children, we presented them what we believe to be creative and constructive activities [22] to complete. The

6

Joana Campos, Ana Paiva

toolkit offered activities for thinking (Bubbles), mapping (Social Network), feeling (Feelings and Anger Measurement), storytelling (You’re the writer), being creative and constructive (No Rules and Journal) (inspired on Sanders idea of Strategic Visioning Workshops [17]).

Social Network (Participants) A key aspect of the dynamics of interpersonal conflict is how people relate with each other and how their relationships change over time as a consequence of conflict episodes. The first task was designed to get information about children’s social relationships. The social network task asks about the participants emotional links to other children in school. At the same time, it tries to capture the degree of closeness associated with different emotions. Figure 3 depicts the provided material for this task. The yellow envelope contains a card that has the instructions for this activity, a piece of paper with concentric circles, and a set of stickers. Each sticker has an emotion/feeling and a line. The children have to write on that line the name of a person in their school that makes or made them feel that way. Then, they have to choose one of the concentric circles on which to affix it. The further from the centre, the less close to them that person is.

Fig. 3. Materials for the Social Network Task

Feelings (Outcomes) This task is complementary to the first one and tries to capture triggers for emotions other than anger, which is the emotion most frequently associated with conflict. This and the social networks task were assigned in the first week, as they are both quite simple. Children were asked to match a picture (sad, afraid, happy) with a reason for feeling that way, as shown in Figure 4.

Bubbles (Causes and Strategies) Describing scenarios from pictures is a suitable technique for broadening children’s vocabulary, as suggested by Kreidler [12]. In Kreidler methodology a

Interactive Social Agents rooted in Deep Data

7

Fig. 4. Materials for the Feelings Task

picture depicting a conflict was presented to the children, who had to describe conflict related issues such as what was happening or the reason for what had happened. Inspired by this, we asked them to do the opposite, as we do not intend to teach children about conflict (at least at this stage), but to extract realistic conflict scenarios. Our adaptation of this method asked children to use a set of given words for describing a situation (see Figure 5). They were free to draw or write depending on their preference. The words fell into three main categories: feelings – sad, angry, scared, upset, worried; external actions (which have a direct impact on the situation) – I said, I did, I had; and internal triggers (which may influence the chosen action) – I felt, I thought, I wanted. The words friend and school were also added. The words were general enough not to guide a child to any particular scenario, as might have happened if we had presented them with a picture. Also note that the word conflict was never mentioned. The answer sheet was divided into three areas identified as Before, What happened, and After. This subdivision may give us not just information about the set of events that lead to a conflict situation, but also the aftermath of the conflict.

You’re the writer (Strategies) Stories and storytelling have had an active role either in ethnography research or in the HCI field, as they are practices claimed to preserve personal expression, relationships, conflicts and multiple perspectives [14].

This task is intended to explore various perspectives on conflict resolution that may be influenced by personality differences, given an initial scenario of a resource dispute. This kind of situation is one of the most usual sources of interpersonal conflict among children [18]. In childrens eyes, it is easily interpreted as a contest in which one party loses and the other wins [12]. Figure 6 shows the material available for this task. At the top of the answer sheet there is a story that describes a conflict between a pair of boys and a pair of girls who are disputing over a computer to finish a school project. Dialogue cues indicate

8

Joana Campos, Ana Paiva

Fig. 5. Materials for the Bubbles Task

some tension between the two pairs, and the story ends up with one of the girls yelling to the boys: �Come on � The children were asked to write a conclusion to the story from this point.

Fig. 6. Materials for You’re the writer Task

Thermomether (Causes and Outcomes) Anger is probably the emotion most intuitively associated with conflict [1].

What makes someone get angry is likely to vary a lot between children. This task was designed to get at the differences in intensity of various triggers for anger. We used an anger thermometer chart, inspired by an activity presented

Interactive Social Agents rooted in Deep Data

9

by Kreidler [12]. In his book, Kreidler’s goal was to expand children’s vocabulary and to help them express what they are feeling. The thermometer is a metaphor for a scale of anger. That is, the hotter it gets the more intense the angry feelings are. The anger scale starts with annoyed, goes through cross, anger, furious and ends with enraged (see Figure 7 above). The children were asked to write down typical situations that make them reach each level of the scale.

Fig. 7. Materials for the Thermometer Task

No Rules (All ) Inspired by the provocative nature of cultural probes and the structure of storyboards, we provided children with a set of materials representative of concepts they already learned in the previous tasks. In the instructions we asked the participant to think about conflict and what that means to him or her. Then, using the provided materials, they are to depict a situation in which a conflict happened or could happen. The envelope is full of stickers (see Figure 8 below), including dialogue bubbles, thermometers at different points of the anger scale, thumbs up/down, and the words what, who, where, and when. (This task was not designed to feed any of conflict dimensions.)

3.6

Implications for design

Information conveyed by the probes redesigns the researchers’ mental models about a certain subject. Yet, how that data encourages subsequent decisions fades away between the two stages. By using this methodology, not only do we want to understand the problem at hand, but also to access real scenarios from the subjective data and make our agents more believable, in the sense that children will be able to identify with them. We do not expect to find a final model for the agents, but rather lay out relevant aspects for a design shift of such characters. Note that the agent community hardly ever designed their agents focusing on actual users during their development. Agents’ behaviours are most of the times generated by the designer’s opinion beforehand.

10

Joana Campos, Ana Paiva

Fig. 8. Materials for the No Rules Task

The design shift that we look for is fed by the children’s emotions, attitudes and actions, which will shape our characters in the game - My Dream Theatre (MDT)1 . How the agent will behave depends to the answer to questions as What are the causes of conflict? What makes them escalate? How do children deescalate? What do they feel afterwards whereas the confrontation was with a friend or other?. The probes contributed to the design, by creating an empathic link with children’s common practices, emotions and perspectives on conflict.

It also allowed us to extract generic information about conflict episodes and vocabulary. The probes helped us to understand the interaction flow and thus stratify how to explain conflict to children, by reinforcing aspects that are not clear to them. For example, from a high level perspective, it helped us to make explicit that conflict is not an isolated act, but rather a phenomenon that evolves throughout several stages. That fact is not usually clear for children.

In the serious game, that idea of a dynamic process was made explicit [2].

That process is driven by the agent’s emotions, which are essential to understand how to manage the conflicts between the characters (see Figure 9). Further, events that occur within the game are closely related to episodes reported in the probes, as an attempt to create a meaningful experience. Additionally, as children more often than not do not employ effective strategies to cope with their conflicts, the game presented to them an array of choices, extracted from the literature (Thomas-Kilmann Model [19]), to help them manage conflict episodes in MDT. In that way, theory and deep data combined provide an environment in which current practices meet other perspectives of conflict, to help children to reason about conflict episodes more effectively.

4

Case Study 2: Capturing Low-Level Deep Data

As previously asserted, despite the massive research on conflict in the social sciences, there is still some uncertainty on how to translate the theory to more 1

Graphics by Serious Games Interactive (SGI)

Interactive Social Agents rooted in Deep Data

11

Fig. 9. An agent’s emotional reaction due to incompatible goals in the MDT game. In this case, the character ‘Maria’ is overtly manifesting her anger (her emotional state is given by the thermometer). Unfortunately, the player was not able to prevent the agent’s emotional escalation.

specific parameters that together provide an adequate description of the phenomenon. The lack of a blue-print to represent conflict in interactive settings has challenged researchers to find possible ways of representing conflict for their purposes. To create believable virtual agent’s in conflict we explore how an agent knows that it is in conflict, by exploring a human-human interaction in a mixedmotive game. Not only are we interested in understanding how one perceives she is in conflict, but also what are the interpersonal strategies applied as part of human adaptation to the interactional partner. We should not forget that conflict is more than a state of affairs or an overt manifestation of disagreement, it is a dynamic process in which is not guaranteed that the parties will go through all stages towards climax. In fact, overt manifestations of conflict are not as usual as one might think.

In mixed-motive negotiations, conflicts are bound to emerge as participants have opposed preferences and each one try to maximise their own gains. Such experimental setting, in which potential for conflict exists, acts as a model of a social interaction that is the object of study. For this study we used a variation of the “Game of Nines”.

4.1

The “Game of Nines”

The “Game of Nines” is a mixed-motive bargaining game and it was firstly used

by Kelley et. al [11]. This bargaining game was selected because it creates an interesting setting, where the negotiators face dilemmas concerning their goals and forms of communication. Further, it requires that the players negotiate to divide a joint reward between themselves with competitive and cooperative incentives.

12

Joana Campos, Ana Paiva

Each player holds cards from one to eight in their hands. At each trial, the negotiators must agree on the cards they play such that their sum is nine or less.

In each turn of the game a minimum necessary share (MNS) is assigned to each negotiator. This MNS value is only known by the person to whom it was assigned to. Therefore, the information about the other is incomplete. For a profitable agreement the negotiator has to bargain for a value above the MNS (e.g. if a player has a MNS equals to 4 and plays a 6 he will get 2 as a reward), without knowing the extent of concessions that the other can make. If the participants do not reach an agreement in a limited amount of time both get zero. Therefore, is of mutual interest to get to an agreement and it is in each person’s individual interest to chose a division that is the most profitable to her (and thus, minimally profitable to the other).

As the negotiators are children we eliminated the time constraint2 . Because of that, if children were not able to reach an agreement they both would get zero and “the bank” would win as many points as the difference between 9 and the sum of their MNS values. In the end, both players individually have to make more profit than “the bank”. This establishes that the lack of consensus costs and also adds a cooperative incentive, ensuring a mixed-motive relationship.

The experiment consisted in 5 rounds. In each round the participant took a MNS (a card with a number) from an envelop. The child was instructed not to show the card during the trial and never agree on a value below that number. At the end of the round, each participant had to show to the other her MNS value.

During each bargaining round the players had to jointly agree on a possible contract. Each contract corresponds to a card that is going to be played by player A and a card played by player B, so that their sum does not exceed 9.

For example, if player A plays the card 6 a possible contract is player B to play the card 3. The interests of the parties are always directly opposed. What is most profitable for one player is least profitable for the other. Besides holding the cards ranging from one to eight, each player also holds a card that allows them to give up if they feel they are not able to achieve a viable agreement. The rules for not reaching a consensus apply here. The player that sums more points in the end wins the game.

4.2

Procedure

Before the game sessions children filled in a sociometric questionnaire and a personality test. The former was applied, mainly to ensured that children in opposed poles (neglected and populars) or children that did not like each other were not paired together, given the sensitive nature of this experiment3 .

For the experiment, each dyad was collected from the classroom and a bracelet to measure their electrodermal activity was immediately attached to their wrists.

2

3

We verified in pilot sessions that this factor was making them not to pay attention to what was happening in the game.

The results from both questionnaires are beyond of the scope of this paper and are not going to be discussed here.

Interactive Social Agents rooted in Deep Data

13

Then, children were conducted to a room made available for the purpose. The participants were sit face-to-face at the opposite ends of a table, on which a card board was there to assist them through the game. After the explanation of the rules, the participants were “walked through” two rounds of the game to learn its mechanics. After that they were left alone to play the game (Figure 10).

To motivate the participants to do well, we told the players that the person who accumulates more points during the game would win a prize. In the end, both children won a prize, but the winner was able to choose between two options (one item was better than the other).

Fig. 10. Two girls playing the Game of Nines

4.3

Data Collection and Recording

The “Game of Nines” data was collected in a public school in country X. In total, 22 children (13 girls and 9 boys) aged 10 to 12 years-old, participated in dyadic sessions of the game. All dyads, with one exception, are same-sex participants. Opt-out consent forms were provided to all parents or guardians of those children. All games were video and audio recorded.

Two cameras recorded the interaction. Each one of the cameras was directed to one of the children, capturing their face and hand movements. A audio recorder was used due to the poor performance of the cameras microphones. The three elements were later synchronized.

4.4

Annotations

The basic actions of the game were manually annotated along with gaze, smiles and speech transcriptions. Furthermore, action tendencies, emotions and other social signals were also annotated by two psychologists, using the ELAN4 software.

4

http://tla.mpi.nl/tools/tla-tools/elan/

14

Joana Campos, Ana Paiva

The latter set of annotations are based on the EASI Model (Emotions As Social Information Model) presented by Van Kleef and DeDreu [20] who advocate a more social approach to emotions, that is how emotions in a social interaction shape behaviour. That seems appropriate to our analysis, since a true understanding of the deep data requires a focus in interpersonal effects of emotion.

Hence, the annotation layers to describe the behaviour in this social interaction are the following: Emotion arise from an individual’s appraisal of the situation and individuals may use those to their social decisions. Four groups of emotion were considered. Happiness, joy, contentment; Anger, frustration, irritation; Sadness, distress, disappointment and worry; and finally guilt, regret and embarrassment.

Social Signals are related to the emotions. Were considered and annotated signals of affiliation, opportunity, dominance, aggression, supplication and appeasement.

Action Tendencies in this model are conceptualised in terms of Horney’s theory [10]. In decision making, one can be moving towards, enrolling in more cooperative activities, moving away by taking a passive stance in the interaction or moving against the other, in which non-cooperative actions are taken.

These action tendencies framed here in the context of decision making, have been also used to define a taxonomy of conflict behaviour [21] [5].

The model also tries to explain the interaction between these three elements.

The EASI model provides a framework for understanding the effects of one’s emotions in one’s action and the competitive or cooperative nature of the interaction. The focus on interpersonal emotions and their effect on the individual’s actions tendencies may help to explain why some conflicts subside whereas other emerge.

4.5

Implications for design

The employed experimental paradigm attempts to create a natural setting that resembles a real-life situation. It has increased the complexity for interpretation, but on the other hand the findings can be generalised to create more natural situations (e.g. design agent’s decision process such that the agent is able to bypass or ignore a conflict). The analysis of the behaviours according to the EASI model may shed a light on the cooperative and competitive moves present in the interactions between children and how their interpersonal strategies made, most of the times, conflict not to be manifested (e.g. which moves contribute to move forward in the conflict process and what moves makes it go backwards).

Guided by this model, we attempt to shed a light in conflict dynamics and its underlying processes for a more natural agent’s behaviours. Furthermore, during evaluation data can be used to verify the acceptability of the interaction, instead of asking users directly about their impression of the agent’s behaviour [16].

Interactive Social Agents rooted in Deep Data

5

15

General Discussion and Conclusions

Deep data from human-human interactions serves as an useful tool for developing and modelling IVAs. In this paper we explore two experimental paradigms to access users’ perceptions, emotions and motivations in two different levels of granularity, for two different purposes. On one hand, we explored how high-level deep data can inform us about children’s perspective of conflict. Conflict Probes played a reinvigorating role in our pre-inventive design phase. The theory that grounded the tasks’ structure gave us the opportunity to learn about conflict. It also helped us understand how conflict can be explained to children, which had a direct effect on the design of the agents in the game. The probes’ returns verified our earlier findings in showing the unsophisticated knowledge children have about conflict and the difficulty they have in recognizing its various dimensions as part of a coherent whole.

To explore a different angle, we presented an experiment, where we try to replicate a situation in which participants do not have full and accurate insight into the structure of the social situation nor the information necessary to resolve it effectively. The Game of Nine reduces real-life to a mixed-motive game, in which children apply interpersonal strategies to adapt to the other. Our aim is to explore the interaction in term of emotions, social signals and action tendencies according to the EASI Model to better understand how conflict unrolls. Adding to this, other social signals as gaze cues, smiles and electrodermal activity may also help to make of the “fuzzy” dyadic interaction.

It is our belief that different granularities of data are required to represent more natural IVAs that suit the human standards. Data can be useful at different stages of design for different purposes as described in this paper. Yet, theory from different areas of expertise is also essential to model agent’s behaviours. In fact, theory and data combined are essential tools for building more natural agent’s behaviours.

Acknowledgements This work was supported by national funds through FCT - Funda¸c˜ao para a Ciˆencia e Tecnologia, under project PEst-OE/EEI/LA0021/2013 and a scholarship (SFRH/BD/75342/2010) granted by FCT.

References 1. R. F. Baumeister, A. Stillwell, and S. R. Wotman. Victim and perpetrator accounts of interpersonal conflict- autobiographical narratives about anger.pdf. Journal of Personality and Social Psychology, 59(5), 1990.

2. J. Campos, C. Martinho, G. Ingram, A. Vasalou, and A. Paiva. My dream theatre: Putting conflict on center stage. In Proceedings of the 8th International Conference on the Foundations of Digital Games, 2013.

16

Joana Campos, Ana Paiva

3. J. Campos, C. Martinho, and A. Paiva. Conflict inside out: a theoretical approach to conflict from an agent point of view. In Proceedings of the 12th International Conference on AAMAS, 2013.

4. C. Castelfranchi. Conflict ontology. Computational Conflicts, Conflict Modelling for Distributed Intelligent Systems, pages 21–40, 2000.

5. M. Deutsch, P. T. Coleman, and E. C. Marcus. The handbook of conflict resolution: Theory and practice. Jossey-Bass, 2006.

6. B. Endrass, E. Andr´e, L. Huang, and J. Gratch. A data-driven approach to model culture-specific communication management styles for virtual agents. In Proceedings of the 9th International Conference on AAMAS, 2010.

7. B. Endrass, E. Andr´e, M. Rehm, and Y. Nakano. Investigating culture-related aspects of behavior for virtual characters. Autonomous Agents and Multi-Agent Systems, 2013.

8. W. W. Gaver, T. Dunne, and E. Pacenti. Cultural probes. Interactions, 11(5), 1999.

9. T. Hemmings, A. Crabtree, T. Rodden, K. Clarke, and M. Rouncefield. Probing the probes. In Proc. of Participatory Design Conference, 2002.

10. K. Horney. Our inner conflicts: A constructive theory of neurosis. WW Norton & Company, 1945.

11. H. H. Kelley, L. L. Beckman, and C. S. Fischer. Negotiating the division of a reward under incomplete information. Journal of Experimental Social Psychology, 3(4), Oct. 1967.

12. W. J. Kreidler. Conflict Resolution Through Children’s Literature. Scholastic Professional Books, 1994.

13. L.-P. Morency, I. de Kok, and J. Gratch. A probabilistic multimodal approach for predicting listener backchannels. Autonomous Agents and Multi-Agent Systems, 20(1), 2010.

14. M. J. Muller. Participatory design: the third space in HCI. 2003.

15. H. J. Mller and R. Dieng. On conflicts in general and their use in AI in particular.

In Computational Conflicts: Conflict Modeling for Distributed Intelligent Systems, with Contributions by Numerous Experts. Springer, 2000.

16. M. Rehm and E. Andr. From annotated multimodal corpora to simulated humanlike behaviors. In Modeling Communication with Robots and Virtual Humans.

Springer, 2008.

17. E. Sanders. Generative tools for codesigning. In Collaborative Design. London: Springer-Verlag, 2000.

18. C. U. Shantz. Conflicts between children. Child Development, 58(2), 1987.

19. K. W. Thomas. Conflict and conflict management. In Handbook of Industrial and Organizational Psychology, Dunnette, M.D. (Ed.). Randy McNally, Chicago, 1976.

20. G. A. Van Kleef, C. K. W. De Dreu, and A. S. R. Manstead. An interpersonal approach to emotion in social decision making: The emotions as social information model. Advances in experimental social psychology, 42, 2010.

21. E. V. d. Vliert. Complex Interpersonal Conflict Behaviour: Theoretical Frontiers.

Psychology Press, 1997.

22. P. Wyeth and C. Diercke. Designing cultural probes for children. In Proceedings of the 20th conference of the computer-human interaction special interest group (CHISIG) of Australia - OZCHI, page 385, New York, USA, 2006. ACM Press.