Building an On-Demand Avatar-Based Health Intervention for Behavior Change

Christine Lisetti, Ugan Yasavur,Claudia de Leon, Reza Amini, Napthali Rishe

School of Computing and Information SciencesFlorida International University

Miami, FL, 33199, USA

Ubbo VisserDepartment of Computer Science

University of MiamiCoral Gables, FL, 33146, USA

Abstract

We discuss the design and implementation of the pro-totype of an avatar-based health system aimed at pro-viding people access to an effective behavior changeintervention which can help them to find and cultivatemotivation to change unhealthy lifestyles. An empathicEmbodied Conversational Agent (ECA) delivers the in-tervention. The health dialog is directed by a compu-tational model of Motivational Interviewing, a noveleffective face-to-face patient-centered counseling stylewhich respects an individual’s pace toward behaviorchange. Although conducted on a small sample size, re-sults of a preliminary user study to asses users’ accep-tance of the avatar counselor indicate that the systemprototype is well accepted by 75% of users.

1 IntroductionRecent epidemics of behavioral issues such as excessive al-cohol, tobacco, or drug use, overeating, and lack of exerciseplace people at risk of serious health problems (e.g. obe-sity, diabetes, cardiovascular troubles). Medicine and health-care have therefore started to move toward finding ways ofpreventively promoting wellness rather than solely treatingalready established illness. Health promotion interventionsaimed at helping people to change lifestyle are being de-ployed, but the epidemic nature of these problems calls fordrastic measures to rapidly increase access to effective be-havior change interventions for diverse populations.

Lifestyle changes toward good health often involve rela-tively simple behavioral changes that can easily be targeted(as opposed to major life crisis that usually require psy-chotherapy): for example lower alcohol consumption, ad-just a diet, or implement an exercise program. One of themain obstacle to conquer change of unhealthy habits, is of-ten ’just’ a matter of finding enough motivation. There hasrecently been a growing effort in the health promotion sec-tor to help people find motivation to change, and a numberof successful evidence-based health promotion interventionshave been designed.

Our current research interests have brought us to considerone such counseling style for motivating people to change –namely Motivational Interviewing (MI) (Miller and Rollnick

Copyright c© 2012, Association for the Advancement of ArtificialIntelligence (www.aaai.org). All rights reserved.

2002; Miller and Rose 2009). MI is a recently developedstyle of interpersonal communication which was originallyconceived for clinicians in the field of addiction treatment,and which has been since then proven useful for medicaland public health settings (Emmons and Rollnick 2001), aswell as for concerned others. It is aimed at helping clients(or people in general) to find the motivation to change theirlifestyle with respect to a specific unhealthy pattern of be-havior. Although originally conceived as a preparation forfurther treatment, enhancing motivation and adherence, re-search revealed that change often occurred soon after one ortwo session of MI, without further treatment, relative to con-trol groups receiving no counseling at all (Miller and Roll-nick 2002).

At the same time as novel behavior change interventionshave developed, simulated human characters have becomeincreasingly common elements of user interfaces for a widerange of applications such as interactive learning environ-ments, e-commerce and entertainment applications such asvideo games, virtual worlds, and interactive drama. Simu-lated characters that focus on dialog-based interactions arecalled Embodied Conversational Agents (ECAs). ECAs aredigital systems created with a physical anthropomorphicrepresentation (be it graphical or robotic), and capable ofhaving a conversation (albeit still limited) with a humancounterpart, using some artificial intelligence broadly re-ferred to as an “agent“ (Cassell and J. Sullivan, S. Prevost,& E. Churchill 2000). With their anthropomorphic featuresand capabilities, they provide computer users with a some-what natural interface, in that they interact with humansusing humans’ natural and innate communication modali-ties such as facial expressions, body language, speech, andnatural language understanding (Magnenat-Thalmann, N.and Thalman 2004; Predinger and Ishizuka 2004; Allen etal. 2006; Becker-Asano 2008; Boukricha and Wachsmuth2011; Pelachaud 2009). We posit that given the well-definedstructure of MI (discussed next), MI-based interventions canbe automated and delivered with Embodied ConversationalAgent (ECA). We describe the design and implementation ofan MI-based automated intervention aimed at helping peoplewith some excessive alcohol consumption to become awareand potentially motivated to change unhealthy patterns ofbehavior.

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2 Related ResearchSeveral avatar-based health intervention or counselingapplications have been developed and evaluated. Silvermanand colleagues coupled a simulation package with an an-imated pedagogical heart-shaped avatar to promote healthbehavior change in heart attacks (Silverman et al. 2001).Anthropomorphic pedagogical agents are used in a systemcalled DESIA for psychosocial intervention deployed onhand-held computers in order to teach caregivers problemsolving skills (Johnson, LaBore, and Chiu 2004). FitTrackuses an ECA to investigate the ability of an avatar-based sys-tem to establish and maintain a long-term working alliancewith users (Bickmore and Picard 2005). The Virtual Hospi-tal Discharge Nurse has been developed to explain writtenhospital discharge instructions to patients with low healthliteracy. It is available to patients on their hospital beds andhelps to review material before discharging them from thehospital. It indicates any unresolved issues for the humannurse to clarify (Bickmore, Pfeifer, and Jack 2009).

More recently MI has been identified as particu-larly useful for health dialog systems (Lisetti 2008;Lisetti and Wagner 2008). Since then some researchers haveexplored the use of 2-D cartoon or comic-like characters toprovide MI-related material to patients or to train health carepersonnel in MI (Schulman, Bickmore, and Sidner 2011;Magerko et al. 2011). Although the avatars employed inthese mentioned systems have shown some promisingacceptance by their users, because they are 2D, they lackdynamic expressiveness which is key for communicatingfacial affect (Ehrlich, Schiano, and Sheridan 2000), itselfessential in establishing the MI requirement of empathiccommunicative style (Miller and Rollnick 2002).

Motivational Interviewing (MI) has been defined byMiller and Rollnick (Miller and Rollnick 2002) as a direc-tive, client-centered counseling style for eliciting behaviorchange by helping clients to explore and resolve ambiva-lence. One of MI central goals is to magnify discrepanciesthat exist between someone’s goals and current behavior.MI basic tenets are that a) if there is no discrepancy, there isno motivation; b) one way to develop discrepancy is to be-come ambivalent; c) as discrepancy increases, ambivalencefirst intensifies; if discrepancy continues to grow, ambiva-lence can be resolved toward change. In the past few years,adaptations of motivational interviewing (AMIs) have mush-roomed with the purpose to meet the need for motivationalinterventions within medical and health care settings (Burke,B. L. and Arkowitz, H. and and Dunn 2002). In such set-tings, motivational issues in patient behavior are quite com-mon, but practitioners may only have a few minutes to dealwith them. Some of these AMIs can be as short as one 40-minute session.

3 Health Counselor System ArchitectureWe based our system on one such AMI referred to as theCheck-Up that specifically targets excessive drinking behav-iors and with which people can reduce their drinking by anaverage of 50% (Miller, W., Sovereign, R. and Krege 1988;

Hester, R. K., Skire, D.D., and Delaney 2005). The check-up (along with modifications of it) is the most widely usedbrief motivational interviewing adaptation, where the client(usually alcohol or drug addicted) is given feedback, in aMI “style“based on individual results from standardized as-sessment measures (Burke, B. L. and Arkowitz, H. and andDunn 2002).

The On-demand Health Counselor System Architecturewe developed can deliver personalized and tailored behav-ior change interventions using the MI communication stylevia multimodal verbal and non-verbal channels. The systemis developed in the .NET framework as a three-tier architec-ture which uses third party components and services.

The system architecture is composed of the main mod-ules shown in Figure 1, which we describe in details later. Inshort, during any interaction, the user’s utterances are pro-cessed by the Dialog Module which directs the MI sessionsand elicits information from the user; the user’s facial ex-pressions are captured and processed by the Affective Mod-ule, in an attempt to assess the user’s most probable affectivestates and convey an ongoing sense of empathy via a Mul-timodal Avatar-based Interface (using 3D animated virtualcharacter with verbal and nonverbal communication); Psy-chometric Analysis is performed based on the informationcollected by the Dialog Module, and its results are stored inthe User model which is maintained over multiple sessionsto offer a dynamically Tailored Intervention in the form ofsensitive feedback or specific behavior change plans.

Behavior Change Planner

UserModel

TailoredFeedback

Engine

PsychometricData

PsychometricAnalysis

Score Evaluator

DialogModule

PsychometricInstruments

MI-based DialogEngine

DialogPlanner

AffectiveModule Empathy

Engine

UserUtterances

User'sFacial

Expression Text-To-SpeechEngine

Multimodal Avatar-basednterface

Avatar SystemFacial

Processing

Tailored ntervention

Figure 1: On-demand Avatar-based Counselor Architecture.

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Dialog ModuleThe Dialog Module evaluates and generates dialog utter-ances using three key components: a Dialog Planner, anMI-based Dialog Engine and a collection of PsychometricInstruments. The interaction of the client with the system isbased on a series of dialog sessions, each of which havinga specific assessment goal to identify different aspects ofthe user’s behavioral problem (if any) and in this article wefocus on excessive intake of alcohol. Each session is basedon psychometric instruments which the Dialog Planneruses as a flexible plan so that the dialog-based interactioncan be dynamically conducted based on the knowledge thatthe system has acquired about the user (maintained in theuser model), and on the user’s current entries and affectivestates.

Psychometric Instruments: We use a collection of well-validated Psychometric Instruments commonly used bytherapists to assess an individual’s alcohol use in order todesign each assessment session (Miller and Rollnick 2002).Each psychometric instrument contains a set of questionswhich represent the plan for an assessment session. Forexample one instrument (DrInC, 50 items) is used toidentify consequences of the client’s drinking problem withfive different parameters (e.g. interpersonal relationships,work) and these are stored in the user model (see below).

Dialog Planner: This component of the dialog moduledecides what type of utterance is to be generated based onthe previous interaction in that session, i.e. it decides thenext stage of the session. For instance, when the systemneeds to assess the client’s awareness of the consequencesof his/her drinking, the The Drinker Inventory of Conse-quences (DrInC) psychometric instrument is used as theplan for the session, and each question is assigned one of thefive topics of the DrInC (e.g. inter-personal, intra-personal).To measure consequences in each area, there are sets ofnon-sequentially positioned questions for each area andthese questions are conceptually related with each other.The dialog planner aims at detecting discrepancies betweenreceived answers for questions in the same area. If theplanner detects discrepancy in the client’s answers, thenthe MI-based Dialog Engine is invoked to generate someMI-style dialog, such as reflective listening (explainednext). If no such discrepancy nor ambivalence in the user’sanswers is detected, then the dialog planner continues withthe assessment session.

MI-based Dialog Engine: This engine applies a finite set ofwell-documented MI techniques known under the acronym(OARS): Open-ended questions, Affirmations, Reflective lis-tening, and Summaries, which are applied toward goals con-cerning specific behaviors (e.g. excessive alcohol use, druguse, overeating). The engine adapts each static psychometricinstrument to MI style interaction and currently implements1) affirmations (e.g.“You have much to be proud off”); 2)Summaries (“Ok, so let me summarize what you’ve told metoday about your experience during the last 90 days. Youhave had more than 20 drinks, during the last 90 days you

have missed work about once every two weeks, you havegotten into arguments with friends frequently at parties, ...”);and 3) reflective listening.

Reflective Listening is a client-centered communicationstrategy involving two key steps: seeking to understand aspeaker’s thoughts or feelings, then conveying the idea backto the speaker to confirm that the idea has been understoodcorrectly (Rogers 1959). Reflective listening also includesmirroring the mood of the speaker, reflecting the emotionalstate with words and nonverbal communication (which wediscuss in the Affect Module below). Reflective listening isan important MI technique which the system simulates tohelp the client notice discrepancies between his or her statedbeliefs and values, and his or her current behavior. We cur-rently implement simple reflection to elicit ambivalence (e.g.– Client: “I only have a couple of drinks a day.” – Counselor:“ So you have about 14 drinks per week.”).

We also use Double-sided reflection to summarize am-bivalence, which can help to challenge patients to workthrough their ambivalence about change. Statements such as“on the one hand, you like to drink and party, but on the otherhand, you are then less likely to practice safe sex” can clearlysummarize a patient’s ambivalence toward change (Botelho2004). Other double-sided reflections such as “Ok, so yourwife and children complain daily about your drinking butyou do not feel that your ability to be a good parent is atstake, is that right?”, can magnify the discrepancies betweenthe user’s current situation and what they would like it to be.

Psychometric AnalysisAs shown in Figure 1, psychometric analysis is concernedwith processing Psychometric Data collected in the dia-log module using the collection of psychometric instruments(described earlier). Based on instructions in each instru-ment, the Score Evaluator module calculates the score ofthe user for a particular measuring instrument, according tonormative data (MATCH 1998). The result is stored in theuser model (see next) in order to identify specific aspects ofthe drinking problem. For instance, the instrument assessingdrinking patterns can locate the user on a spectrum of al-cohol consumption (low, mild, medium, high, or very high,where a score above 8 indicates mild or higher problems).When provided feedback about this score (by the TailoredFeedback module), the user can make an informed decisionabout whether or not to continue with the offered self-helpprogram. Respecting the MI spirit, the user is never coercedand can at any moment exit the intervention and the systemgracefully terminates the conversation.

Tailored InterventionUser Model: The user model enables the system to tailorits intervention to a specific user. Tailoring can be achievedin multiple ways: by using the user’s name referred to aspersonalization, by using known characteristics of the usersuch as gender referred to as adaptiveness, and by using self-identified needs of the user referred to feedback-provision.

The user model collects the age, gender, weight, heightand first name or nickname to preserve anonymity. It alsogathers the user’s ethnicity to implement patient-physician

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ethnic concordance with diverse avatars matching the eth-nicity of the user because it is known that racial concor-dance increases the quality of patient-physician communica-tion and some patient outcomes (e.g. patient positive affect)(Cooper et al. 2003). In addition to static demographic infor-mation, the user model builds and dynamically maintains theprofile of each user as it changes over time. The user modelincludes different variables in six different areas: motivation,personal attributes, frequency of drinking, risk factors, con-sequences and dependence. Each area consists of multiplevariables to assess specific aspects of the problem from dif-ferent view points. These psychometric data are calculatedbased on psychometric instruments (see above).

Because the intervention is designed to assist the userto change unhealthy behavioral patterns, it can be usedover multiple sessions as on-demand follow-up sessions.As the user continues to interact with the system overtime, the user model keeps track of the fluctuating user’sprogress toward change in terms of: the user’s currentlevel of ambivalence, by identifying and weighing positiveand negative things about drinking; the user’s readiness tochange, from pre-contemplation to maintenance (Prochaskaand Velicer 1997); as well as the result of other specializedself-report questionnaires (e.g. the user’s awareness of theconsequences of drinking).

Tailored Feedback Engine: This engine generates tailoredfeedback based on the user model. Several studies showedthat tailored health communication and information can bemuch more effective than non-tailored one because it isconsidered more interesting and relevant, e.g. (Noar, Benac,and Harris 2007). Because the feedback module revealsdiscrepancies between the user’s desired state and presentstate, it is likely to raise resistance and defensiveness. Thestyle in which the feedback is given therefore needs to beempathetic enough to avoid generating defensiveness. Anumber of pre-identified sub-dialog scripts are used forthe various topics of feedback, e.g. self-reported drinking,perceived current and future consequences of drinking,pros and cons of current consumption patterns, personalgoals and their relation to alcohol use, readiness to changedrinking behavior. The user receives feedback about theassessed self-check information, which is put in perspectivewith respect to normative data (e.g. “according to thecurrent national norm, 10% of people your age drink asmuch as you do”).

Behavior Change Planner: This component helps to createpersonalized behavior change plans for clients who are readyto change, and is only entered by users who have receivedfeedback from the Tailored Feedback Engine. The first stepof the behavior change planner is to assess the user’s readi-ness to change along a continuum from “Not at all ready” to“Unsure” to “Really ready to change”. Depending upon thelevel of readiness, different sub-modules are performed.

Avatar-based Multimodal User InterfaceThe user interface of the system is web-based with an em-bedded anthropomorphic Embodied Conversational Agent

Figure 2: Dynamic Facial Expression Animations

(ECA) or avatar, which can deliver verbal communicationwith automatic lip synchronization, as well as non-verbalcommunication cues (e.g. facial expressions).

Avatar System: We integrated a set of features that weconsider necessary for health promotion interventions in ourcurrent prototype (using a third party avatar system calledHaptek): 1) a 3-dimensional (3D) graphical avatar well-accepted by users as documented in an earlier study (Lisettiet al. 2004); 2) a selection of different avatars with differentethnicity (e.g. skin color, facial proportions) shown in Fig-ure 3 (Nasoz, F. and Lisetti 2006); 3) a set of custom-madefacial expressions capable of animating the 3D avatar withdynamical believable facial expressions(e.g. pleased, sad,annoyed shown in Figure 2) (Paleari, Grizard, and Lisetti2007); 4) a Text-To-Speech (TTS) engine able to read textwith a natural voice with lip synchronization; and 5) a setof mark-ups to control the avatar’s TTS vocal intonation andfacial expressions in order to add to the agent’s believability(Predinger and Ishizuka 2004).

Affective ModuleOur on-demand avatar counselor aims at delivering healthpromotion messages that are not only tailored to the user (viathe user-model), but also empathetic. Because discussing is-sues about at-risk behaviors are highly emotional for peopleto talk about (e.g. embarrassment, discouragement, hope-fulness), our context or domain application is particularlysuited to inform the design of empathic characters. Empathyis considered the most important core condition in terms ofpromoting positive outcomes in psychotherapeutic contexts(Bellet and Maloney 1991) and it can account for as muchas 25%-100% rate of improvement among patients (Millerand Rollnick 2002; Rogers 1959).

Currently, the Empathy Engine is limited to decidingwhich facial expressions the avatar needs to display. Our cur-rent system focusses on mimicking the user’s facial expres-sions, which is considered as a type of primitive empathy(Boukricha and Wachsmuth 2011). In the Facial Process-ing component, pictures of the user’s face are taken at fixedintervals during the user’s interaction with our avatar-basedsystem, and these are analyzed and labeled with categori-cal emotion labels (neutral, sad, happy, surprised, and an-gry) using the face recognition engine from (Wolf, Hassner,and Taigman 2011). Since health counselors only carry out

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Figure 3: User can choose race-concordant avatar.

partial mimicry to retain a positive overtone during the inter-action, we model semi-mimicry so that the avatar displays asad expression if the user’s expression is identified as sad,and smiling or neutral otherwise.

4 Avatar Evaluation ResultsIn a recent survey study, eleven individuals with differentlevels of technicality were asked to interact with the systemand evaluate their experience based on functionality and onhow much the users liked interacting with the avatar.

Specific results such as the willingness of individuals todiscuss alcohol consumption with an embodied conversa-tional agent (ECA) showed that 75% of users felt as com-fortable or more comfortable interacting with the avatarcounselor than they would with a human counselor:37.5% felt as comfortable, 37.5% felt even more comfort-able, and a remaining 25% said it was less comfortable thanwith a human.

Although the small group that was uncomfortable with theECA argued that body language – currently lacking – wasimportant to obtain feedback, the groups in favor pointedout that because the avatar could not judge nor embarrassthem, they felt more positive and comfortable. These resultsare in line with research findings that patients that engage inbehavior that can put them at risk (e.g. excessive drinking,unsafe sex, over-eating) tend to report more information to acomputer interviewer than to its human counterpart (Servan-Schreiber 1986). The subjects in favor of the ECA interac-tion further pointed out the benefit of the system in termsof availability when human counselors are not present, orsimply not needed at all times.

In a general context, the statistic of whether individualswould consider an ECA to discuss personal topics was73% positive. According to 64% of individuals, the imple-mentation of this system using animated avatars made theinteraction engaging. This fact can become substantial toaddress high attrition rates found in textual computer-basedhealth intervention (Pallonen et al. 1998).

The availability of a variety of avatars based on race,gender and physical appearance caused a positive impacton individuals with a 91% rate of acceptance of diverseavatars. People liked the fact that they could choose which

avatar to interact with, building that way, a patient-physicianconcordance that could lead to a strong working alliance(Cooper et al. 2003).

Lastly, modeling empathy for the patient was a key pointwhen individuals were asked how they felt about the level ofexpression of emotions of the avatars. A 45% agreed on thesuitability of the expression of emotions while a 36% saidthe avatar’s facial expressions were not intense enough.When the last group of individuals was asked why the levelof expressions was not appropriate, they argued the need formore positive emotions such as smiles and body movement.

Although the number of subjects was small, these resultsseem to indicate that using avatars for health promoting in-terventions might lead to user’s higher engagement, lowerattrition rates than in text-only computer-based health sys-tems, and overall increased divulgation of sensitive issuesdue to increased confidentiality.

5 ConclusionThe demand for health promotion interventions that are tai-lored to an individual’s specific needs is real, and the tech-nological advance that we described to deliver MI behaviorchange interventions with avatars is an attempt to addressthat need. From our current promising results, we anticipatethat avatar health counselors will be very well accepted byusers once the field has matured from further research on thetopic. We hope this research will contribute to promotinguser’s engagement with health intervention program, and,ideally, increases positive outcomes.

6 AcknowledgementsWe would would like to thank Eric Wagner for introducingus to MI and how it is used in his NIAAA-funded adolescentbehaviors and lifestyle evaluation (ABLE) program. This re-search was supported in part by NSF grants HRD-0833093.

ReferencesAllen, J.; Ferguson, G.; Blaylock, N.; Byron, D.; Cham-bers, N.; Dzikovska, M.; Galescu, L.; and Swift, M. 2006.Chester: Towards a personal medication advisor. Journal ofBiomedical Informatics 39(5):500–513.Becker-Asano, C. 2008. WASABI: Affect simulation foragents with believable interactivity. Ph.D. Dissertation, Uni-versity of Bielefeld, Faculty of Technology.Bellet, P., and Maloney, M. 1991. The importance of em-pathy as an interviewing skill in medicine. Journal of theAmerican Medical Association 266:1831–1832.Bickmore, T., and Picard, R. 2005. Establishing andmaintaining long-term human-computer relationships. ACMTransactions on Computer-Human Interaction (TOCHI)12(2):293–327.Bickmore, T.; Pfeifer, L.; and Jack, B. 2009. Taking the timeto care: empowering low health literacy hospital patientswith virtual nurse agents. In Proceedings of the 27th interna-tional conference on Human factors in computing systems,1265–1274. ACM.

179

Botelho, R. 2004. Motivational Practice: PromotingHealthy Habits And Self-care Of Chronic Diseases. MHHPublications.Boukricha, H., and Wachsmuth, I. 2011. Empathy-BasedEmotional Alignment for a Virtual Human: A Three-StepApproach. KI - Kunstliche Intelligenz 25(3):195–204.Burke, B. L. and Arkowitz, H. and and Dunn, C. 2002. TheEfficacy of Motivational Interviewing and Its Adaptation.In Motivational Interviewing: Preparing People for Change.New-York, NY: Guilford Press, 2nd editio edition. 217–250.Cassell, J., and J. Sullivan, S. Prevost, & E. Churchill, E.2000. Embodied Conversational Agents. Social Psychology40(1):26–36.Cooper, L.; Roter, D.; Johnson, R.; Ford, D.; Steinwachs,D.; and Powe, N. 2003. Patient-centered communication,ratings of care, and concordance of patient and physicianrace. Annals of internal medicine 139(11):907–15.Ehrlich, S.; Schiano, D.; and Sheridan, K. 2000. Commu-nicating facial affect: It’s not the realism, it’s the motion. InCHI’00 extended abstracts on Human factors in computingsystems. NY: ACM Press.Emmons, K. M., and Rollnick, S. 2001. Motivational in-terviewing in health care settings. Opportunities and limita-tions. American journal of preventive medicine 20(1):68–74.Hester, R. K., Skire, D.D., and Delaney, H. 2005. TheDrinker’s Check-up: 12-month outcomes of a controlledclinical trial of a stand- alone software program for problemdrinkers. Journal of Substance Abuse Treatment 28(2):159–169.Johnson, W.; LaBore, C.; and Chiu, Y. 2004. A pedagogicalagent for psychosocial intervention on a handheld computer.In AAAI Fall Symposium on Dialogue Systems for HealthCommunication, 22–24.Lisetti, C. L., and Wagner, E. 2008. Mental health pro-motion with animated characters: Exploring issues and po-tential. In Proceedings of the AAAI Spring Symposium onEmotion, Personality and Social Behavior.Lisetti, C. L.; Brown, S.; Alvarez, K.; and Marpaung, A.2004. A social informatics approach to human-robot inter-action with an office service robot. IEEE Transactions onSystems, Man, and Cybernetics - Special Issue on HumanRobot Interaction. 34(2).Lisetti, C. L. 2008. Embodied Conversational Agents forPsychotherapy. In Proceedings of the CHI 2008 ConferenceWorkshop on Technology in Mental Health.Magerko, B.; Dean, J.; Idnani, A.; Pantalon, M.; andD’Onofrio, G. 2011. Dr. Vicky: A Virtual Coach forLearning Brief Negotiated Interview Techniques for Treat-ing Emergency Room Patients. In AAAI Spring Symposium.Magnenat-Thalmann, N. and Thalman, D. 2004. Handbookof Virtual Humans. Wiley and Sons, Ltd.MATCH, G. P. R. 1998. Matching Alcoholism Treatments toClient Heterogeneity: Project MATCH Three-year DrinkingOutcomes. Alcoholism: Clinical and Experimental Research23(60):1300–1311.

Miller, W. R., and Rollnick, S., eds. 2002. Motivationalinterviewing: 2nd Ed. Preparing people to change addictivebehavior. New York: Guilford Press.Miller, W., and Rose, G. 2009. Toward a theory of motiva-tional interviewing. American Psychologist 64(6):527.Miller, W., Sovereign, R. and Krege, B. 1988. Motiva-tional Interviewing with Problem Drinkers: II. The Drinker’sCheck-Up as a Preventive Intervention. Behavioural Psy-chotherapy 16:251–268.Nasoz, F. and Lisetti, C. L. 2006. MAUI avatars: Mirror-ing the user’s sensed emotions via expressive multi-ethnicfacial avatars. Journal of Visual Languages & Computing15 Issue(5):430–444.Noar, S.; Benac, C.; and Harris, M. 2007. Does tailoringmatter? Meta-analytic review of tailored print health behav-ior change interventions. Psychological bulletin 133(4):673.Paleari, M.; Grizard, A.; and Lisetti, C. L. 2007. AdaptingPsychologically Grounded Facial Emotional Expressions toDifferent Anthropomorphic Embodiment Platforms. In Pro-ceedings of the FLAIRS Conference.Pallonen, U. E.; Velicer, W. F.; Prochaska, J. O.; Rossi, J. S.;Bellis, J. M.; Tsoh, J. Y.; Migneault, J. P.; Smith, N. F.;and Prokhorov, A. V. 1998. Computer-based smokingcessation interventions in adolescents: Description, feasibil-ity, and six-month follow-up findings. Informa Healthcare33(4):935–965.Pelachaud, C. 2009. Modelling multimodal expression ofemotion in a virtual agent. Philosophical transactions ofthe Royal Society of London. Series B, Biological sciences364(1535):3539–48.Predinger, H., and Ishizuka, M. 2004. Life-Like Characters.Springer, NY.Prochaska, J. O., and Velicer, W. F. 1997. The transtheoret-ical model of health behavior change. American journal ofhealth promotion AJHP 12(1):38–48.Rogers, C. 1959. A Theory of Therapy, Personalityand Interpersonal Relationships as Developed in the Client-Centered Framework. In Koch, S., ed., Psychology: theStudy of a Science, Vol. 3. New-York: McGraw-Hill. 184–256.Schulman, D.; Bickmore, T.; and Sidner, C. 2011. An Intelli-gent Conversational Agent for Promoting Long-term HealthBehavior Change using Motivational Interviewing. In 2011AAAI Spring Symposium Series, 61–64.Servan-Schreiber, D. 1986. Artificial intelligence and psy-chiatry. Journal of Nervous and Mental Disease 174:191–202.Silverman, B.; Holmes, J.; Kimmel, S.; Branas, C.; Ivins,D.; Weaver, R.; and Chen, Y. 2001. Modeling emotion andbehavior in animated personas to facilitate human behaviorchange: the case of the HEART-SENSE game. Health CareManagement Science 4(3):213–228.Wolf, L.; Hassner, T.; and Taigman, Y. 2011. Effective un-constrained face recognition by combining multiple descrip-tors and learned background statistics. PAMI, IEEE Trans-actions on 33(10):1978–1990.

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