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Emotional Conversational Agents in Clinical Psychology and Psychiatry María Lucila Morales-Rodríguez, Juan Javier González B., Rogelio Florencia Juárez, Hector J. Fraire Huacuja, and José A. Martínez Flores División de Estudios de Posgrado e Investigación, Instituto Tecnológico de Ciudad Madero Ciudad Madero, Tamaulipas, México [email protected], [email protected], [email protected], [email protected], [email protected]

Abstract. This paper is based on a project at the University of Barcelona to develop the skills to diagnose the Generalized Anxiety Disorder (GAD) in students of psychology and psychiatry using a chatbot. The problem we address in this paper is to convert a chatbot in an emotional conversational agent capable of generating a believable and dynamic dialogue in natural language. For it, the dialogues convey traits of personality, emotions and its intensity. We propose to make an AIML language extension for the generation of believable dialogue, this extension will allow to create a more realistic scenario for the student to diagnose the condition simulated by the conversational agent. In order to measure the perception of the emotional state of the ECA expressed by the speech acts a survey was applied. Keywords: Conversational Agent, Personality, Emotions, Natural Language, AIML.

1 Introduction This work is based on a research at the University of Barcelona [1] where a chatbot based on ALICE [2] was developed with the aim to reinforcing the skills of students in clinical psychology to diagnose the Generalized Anxiety Disorder (GAD) disease. The chatbot knowledge base contains information related to GAD symptoms. The chatbot simulates a patient in medical consultation context with the aim of improving students ability to diagnose the disorder through chatbot interaction. Our research aims to improve the student interaction to be more dynamic and believable and therefore more natural, adding personality traits and emotions. The speech acts currently used by the chatbot lacks of personality traits and emotions. We propose an architecture to evolve from a chatbot to an Embodied Conversational Agent (ECA) with the ability to express emotions and personality traits through written texts. These texts are implemented on AIML language (Artificial Intelligence G. Sidorov et al. (Eds.): MICAI 2010, Part I, LNAI 6437, pp. 458–466, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Markup Language). An Embodied Conversational Agent is an agent that can interact in face-to-face conversations. The aim is to capture the riches and dynamism of human behavior [3]. We used images of a virtual human to reinforce the emotion and intensity of the emotion in the written texts. This paper is structured as follows: Section two presents related work. Section three defines the architecture of the proposed solution. Section four discusses the preliminary results obtained. Section five presents conclusions and future work.

2 Related Works In the literature review we found research about ECAs incorporating AIML, personality and emotions. AIML language is an XML specification, which is useful for programming robots or talking chatbots [2], and it was developed by the free software community Alicebot and Dr. Richard S. Wallace, during the period 1995-2000. This section presents works in which emotions are recognized and expressed through written texts using AIML. Tee Connie et al [4] present an agent to identify and classify the user emotions through the written texts in a conversation. It was implemented in AIML and based on ALICE chatbot [2]. Huang et al [5] used AIML language to incorporate parameters for controlling the non-verbal inputs and outputs into response dialogues. The aim is to maintain empathy during the conversation. On the other hand, there are papers that incorporate an emotional model for implementing personality traits and emotions. We found the Stefan Kopp work [6] where an emotional model was developed. It includes happiness, sadness and anger emotions which control the behavior and the cognitive process of the agent. The Nasser work [7] consists in develop an agent, which through fuzzy system, simulates the emotion of anger incorporating personality traits. The personality was based on the five-factor model (FFM) [8]. The agent expresses different behaviors and levels of anger depending on their personality and emotional state. In some research, both AIML extensions and emotional models are implemented for emotional dialogues generation. In the Eva Cerezo work [9], a virtual agent to control a remote domotic system was implemented. Virtual agent emotional state is controlled by a variable. An AIML language extension was implemented with the new emotional tags incorporation. The virtual agent selects responses according to its emotional state. In the Sumedha Kshirsagar work [10], an emotional agent with personality traits, moods and emotions is deployed. The personality model was based on the FFM (Five Factors Model) and it was implemented using Bayesian networks (Bayesian Belief Network). The emotions are based on the Ortony, Clore and Collins (OCC) model. A text processing module, based on Alice, was implemented. An AIML language extension was done with the incorporation of emotional tags associated to probability values.

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3 Architecture Our architecture to conform the ECA is divided into two interconnected parts: The first part refers to the cognitive-emotional module. This module performs cognitive evaluations and it updates the emotional state according to the ECA personality. The second part consists of a dialogue module based on AIML. This module contains the knowledge base, processes the written text inputs of the user and manages the response selection process. The response selection process is influenced by the emotional state. Fig. 1 shows the general diagram of the process.

Fig. 1. Main Process Diagram

3.1 Emotional Module To control the emotional state we used the circumflex model proposed by Morales [11]. This model is symbolized by a continuous circle, on which are placed orthogonal axes representing psychological concepts. In this model the emotions are placed. Fig. 2 shows this model and the distribution of the four universally recognized emotions, fear, anger, sadness and joy. The new value of an axis is calculated based on its current value and its variation resulted in cognitive evaluation and personality traits. To characterize the personality, the emotional model uses the Five-Factor Model (FFM). The FFM is a model that characterizes 5 personality traits including openness, consciousness, extraversion, agreeableness and neuroticism. The model uses the personality to define the emotional and cognitive attitude type to be transmitted by communication acts of the virtual character. Each context cognitive evaluation process phase, in the emotional model, is influenced by the personality traits of the ECA. The result of this influence will determine an increase or decrease on the value of emotions in the circular model. For example, a person with a high level of neuroticism would tend to increase their level of stress and


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Fig. 2. Emotion Classification Based on the Circumflex Model of Morales [11]

arousal in a new event evaluation. Thus, a person with a high level of agreeableness tends to raise their level of arousal and valence. Fig. 3 summarizes this idea for three of the five values that characterize the personality, for two of the events that may occur.

Fig. 3. Fragment of table that determinate the axes variations of emotions in terms of some of the personality traits in the Emotional Model

The ECA emotional state is updated during the conversation with students based in positive or negative evaluation of events that may occur in the conversation. We define these events through a classification of the ECA conversation topics, some of which will affect their emotional state in a positive or negative form. The emotion process selection is based on the value of the axes which characterizes emotions. The model considers the basic emotions of fear, anger, sadness and joy. These emotions are going to be expressed by the ECA. These emotions, such as it can be seen in Fig. 2, are located in various areas within the circumflex. Thus, a high level in the stress and arousal axes will select the anger

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emotion over any other emotion. In the same way, joy emotion could be chosen through a high level in the valence and stance axes. The attributes, emotion and intensity of emotion, were taken from the emotional model to integrate them into the architecture proposed in this work. These elements are sent to the dialogue module, since they are determinant in the response selection process. 3.2 Dialogue Module Our dialogue module of the knowledge base is based on the AIML corpus designed at the University of Barcelona, which contains information about the daily life of ECA with the aim to express symptoms related to the GAD. The process was conducted in two phases: first, we extended the AIML language incorporating new emotional tags, generating a new knowledge base structure, and second, we defined the new corpus based on this new structure. 3.2.1 Structure of the Knowledge Base AIML is used as a scripting language that defines a database of question-answer, which is used as software for text-based chatbots. The tags most commonly used for this process are , , . An interaction between the chatbot and the user is defined within the element. The possible user expressions are defined in the and the response elements of the chatbot are defined in the element. AIML interpreters seek user input matching with the terms defined in the elements so that the output expression is consistent with the input expression [5]. Fig. 4 shows the structure of these tags.

Fig. 4. Tags Structure in AIML Language

As mentioned, to implement personality traits, emotion and intensity of emotions in the selection process of dialogue, we took the associated attributes with the emotion and intensity of emotion of the emotional model proposed by Morales [11]. These attributes are used in the element. The emotion attribute was incorporated using emotional tags, such as , , , and . The attribute related to the intensity of emotion, is defined as a number ranging between 0 and 100 and represents how strong the emotion is. This attribute is represented by the


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Fig. 5. General Diagram of the AIML Language Extension

Fig. 6. Structure Proposed for the Knowledge Base

tag. In general form, Fig. 5 shows the AIML language extension and Fig. 6 shows the complete structure for the knowledge base. 3.2.2 New Corpus Definition Having defined the knowledge base structure, we proceeded to formalize the original knowledge base into the new structure, the integration of new emotional tags and updating the texts of the existing dialogues according to the new emotional tags. For example, Table 1 shows the feedback for the pattern ¿Do you use drugs? under the following circumstances:

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Emotional State Emotion: Anger Intensity: Low Emotion: Anger Intensity: Medium Emotion: Anger Intensity: High

Answer No, I have never taken drugs, not even smoked. I don’t even smoke! I NEITHER SMOKE NOR DO DRUGS !!!

The code that performs this feedback is presented below: DROGA No, I have never taken drugs, not even smoked. I don’t even smoke! I NEITHER SMOKE NOR DO DRUGS!!! . . .

4 Results Our main interest is validate that students perceive the relationship between the emotional states and the written texts of the ECA, and that they are consistent to the conversation. In order to measure the perception of the emotional state of the ECA expressed by the speech acts a survey was applied. The survey applied to 20 students evaluates the emotions Anger, Joy, Sadness, Fear, Resignation, Distress and Surprise. The survey was divided in two parts and shows the dialogues of a small conversation among a user and the ECA (see figures 7 to 9). In the first part, only written text was used, and in the second one, this is reinforced with images of the ECA using a model included in the 3D animation software Poser 7. User: Why are you here? ECA: Because I get nervous and I suffer all type of symptoms since a long time ago. Fig. 7. ECA's answer driven by a medium intensity of Sadness


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User: Have you had sexual problems? ECA:

Not at all !!!

Fig. 8. ECA's answer driven by a medium intensity of Anger

User: What is your favorite movie? ECA:

My favorite movie is "Bicentennial Man"

Fig. 9. ECA's answer driven by a medium intensity of Joy

The evaluation corresponding to the written text shows that 57.50 % of the emotions were determined correctly. Of this group, only 43.92 % identified correctly the intensity of the emotion that we wanted to express. The evaluation of the speech acts reinforced with images shows that 70.51% identified correctly the emotion and the interpretation of the intensity of the emotion increased to 55.35%. Although the identification of the emotion was improved using images, only three of the seven emotions obtained high percentages of recognition, ranging between 80 % and 97.65 %. The other four emotions evaluated obtained percentages of recognition inferior to 51.25%. Figure 10 shows the percentage obtained for each emotion using written text and images.

Fig. 10. Percentages of success for each emotion using written text and images

Observing the results, we found that it is necessary to identify which factors have an influence in the correct interpretation of the emotions. Although the evaluations show that adding images increases the interpretation rate of the emotional state and its intensity, there are four emotions with a lower percentage of recognition. So, there are new opportunity areas to develop in this area.

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5 Conclusions and Future Work In this paper, we presented an architecture using emotions and personality traits to endow an ECA of emotional dialogues based on AIML. In order to characterize the used phrases, a survey was applied. We evaluated the interpretation of the emotion and its intensity in a dialogue, comparing written text with a version reinforced with images. We noticed that using images increase the percentage of interpretation of emotions and their intensities. However, we identified the need to realize future works to increase the percentage of interpretation of the emotions Resignation, Distress, Sadness and Fear. These emotions obtained a low rate and we need to determine which factors influence the interpretation of these emotions.

References 1. Gutiérrez, J., Alsina, I., Rus, M., Pericot, I.: Virtual Agents for Teaching Diagnostic Skills in Clinical Psychology and Psychiatry. In: CGVR, Las Vegas Nevada, USA (2009) 2. Wallace, R.: Don’t Read Me - A.L.I.C.E. and AIML Documentation. Fecha de consulta 01 de Marzo del 2010 (2000), http://www.alicebot.org/documentation/ dont.html 3. Morales-Rodriguez, M.L., Pavard, B., González-Barbosa, J.J.: Virtual Humans and Social Interaction. In: CGVR 2009, pp. 158–163 (2009) 4. Connie, T., Sing, G.O., Michael, G.K.O., Huat, K.L.: A Computational Approach to Emotion Recognition in Intelligent Agent. In: The Asian Technology Conference in Mathematics (ATCM 2002), Multimedia University, Malacca, December 17-21 (2002) 5. Huang, H., Cerekovic, A., Tarasenko, K., Levacic, V., Zoric, G., Pandzic, I., Nakano, Y., Nishida, T.: Integrating Embodied Conversational Agent Components with a Generic Framework. Published in International Journal of Multiagent and Grid Systems 4(4) (2008) 6. Kopp, S., Becker, C., Wachsmuth1, I.: The Virtual Human Max - Modeling Embodied Conversation. Artificial Intelligence Group, University of Bielefeld, P.O. Box 100131, 33501 Bielefeld, Germany (2006) 7. Ghasem-Aghaee, N., Khalesi, B., Kazemifard, M., Ören, T.I.: Anger and Aggressive Behavior in Agent Simulation. In: Summer Computer Simulation Conference (2009) 8. McCrae, R., John, O.: An Introduction to the Five-Factor Model and Its Applications. Journal of Personality 60, 175–215 (1992) 9. Cerezo, E., Baldassarri, S., Cuartero, E., Serón, F.: Agentes virtuales 3D para el control de entornos inteligentes domóticos. Dept. de Ingeniería de Sistemas e Informática, Universidad de Zaragoza, Instituto de Investigación en Ingeniería de Aragón, I3A (2007) 10. Kshirsagar, S., Magnenat-Thalmann, N.: A multilayer personality model. In: Proceedings of the 2nd International Symposium on Smart Graphics, pp. 107–115. ACM, New York (2002) 11. Morales-Rodriguez, M.L.: Modèle d’interaction sociale pour des agents conversationnels animés. Application à la rééducation de patients cerebro-lésés. PhD Tesis. Toulouse, Université Paul Sabatier, 108 p. (2007)