Agent-based Virtual Humans in Co-Space: An Evaluative Study

Yilin Kang∗, Ah-Hwee Tan∗, and Fiona Fui-Hoon Nah†∗School of Computer Engineering, Nanyang Technological University, Singapore 639798

Email: kang0028, asahtan@ntu.edu.sg† Department of Business and Information Technology

Missouri University of Science and Technology, Rolla MO 65409, USAEmail: nahf@mst.edu

Abstract—Co-Space refers to interactive virtual environmentmodelled after the real world in terms of look-and-feel,functionalities and services. We have developed a 3D virtualworld named Nanyang Technological University (NTU) Co-Space populated with virtual human characters. Three keyrequirements of realistic virtual humans in the virtual worldhave been identified, namely (1) autonomy: agents can functionon their own; (2) interactivity: agents can interact naturallywith players; and (3) personality: agents can exhibit humantraits and characteristics. Working towards these challenges,we propose a brain-inspired agent architecture that inte-grates goal-directed autonomy, natural language interactionand human-like personality. We conducted an evaluative studyinvolving human subjects to assess how virtual characters mayenhance user experience in the virtual world. The results showthat the fusion ART-based virtual humans receive significantlyhigher ratings in various performance measures related to userexperience, especially Telepresence, Perceived Interactivity andFlow.

Keywords-virtual character; Co-Space; evaluation;

I. INTRODUCTIONVirtual worlds have become a popular platform used

in many domains, including education, business, and e-commerce. In our work, we are particularly interested ina special class of virtual worlds called Co-Space, whichrefers to an interactive virtual environment modelled after areal physical world in terms of look-and-feel, functionalitiesand services. Through realistic 3D modeling and animationtechnologies, Co-Space simulates the real world in termsof look-and-feel of the surrounding environment. With theadvancement of pervasive sensor networks, Co-Space mayalso capture and mirror the real world events virtuallyin real time. Besides providing a much faster and easieraccess to information and services, the development of Co-Space offers great opportunities for delivering innovativeapplications and services. Specifically, intelligent agents canbe deployed as virtual humans in Co-Space to enhanceinteractivity and playability.

Despite the appealing potentials [1], a number of chal-lenges must be overcome to make these virtual charactersconvincingly realistic. Firstly, they should be able to functionautonomously within the environment they reside. To do so,they need to understand the context of the environment andthe other virtual characters and human users co-existing in

the environment. Also, the reasoning process for choosingthe appropriate action should preferably not be scripted orpredetermined. Instead, they should function autonomouslyand make decisions proactively in response to events hap-pening in the virtual world around them. Secondly, theinteraction between virtual humans and real humans shouldbe as natural as possible, integrating both verbal and non-verbal communication such as facial expressions and bodygestures. Thirdly, virtual humans should exhibit human-liketraits and characteristics, such as personalities and emotions.All of these require the convergence of a diverse range oftechnologies and domains, including computer graphics, 3Danimation, cognitive science, natural language processing,decision making, dialogue management, speech and gesturegeneration and human-computer interaction which are com-plex to integrate [1], [2]. Working towards these challenges,we propose a brain-inspired agent architecture that integratesgoal-directed autonomy, natural language interaction andhuman-like personality.

Extending from a family of self-organizing neural modelsknown as Adaptive Resonance Theory (ART) [3], [4], thispaper presents an integrated agent architecture that makesdecisions not only based on the situational factors of whatare perceived from the environment but also by taking intoconsiderations of various properties, such as the context ofboth agents and players, as well as their desires, intentions,and personalities. In this way, these agent-based virtualhumans are living a more realistic life by going to classes,having dinner with friends and chit-chatting with others. Tothe best of our knowledge, this is one of the few in-depthworks on building realistic agents with autonomous behav-ior, personification as well as natural dialogue interaction.

As a showcase of our work, we have developed a Co-Space that models the Nanyang Technological University(NTU) campus. We have created realistic 3D models of al-most all locations in NTU and populated the Co-Space withavatars that take on human appearance and body proportion.We have deployed agent based virtual humans into NTU Co-Space. In order to evaluate the effects of virtual humans onuser experience, we have conducted a series of experimentswith users where three instances of NTU Co-Space, eachpopulated with a distinct type of virtual agents in the form of

avatars, are assessed. The first environment (E1) is populatedwith virtual humans that display static messages and do nothave the capability to interact with the users. In the secondenvironment (E2), virtual humans are designed as embodiedconversational agents using AIML [5] technology. The thirdenvironment (E3) is populated with intelligent autonomousvirtual humans with our proposed fusion ART-based agentarchitecture. The experiments involved 58 subjects that wererandomly assigned to one of the three environments. Thesubjects were given the context of looking for an overseasuniversity for an exchange program and were visiting NTUCo-Space to help them in making the decision. Each subjectwas asked to complete a pre-questionnaire and a post-questionnaire before and after they experienced the envi-ronment respectively. We captured seven constructs relatedto their user experience, namely Telepresence, Social Pres-ence, Perceived Interactivity, Perceived Usefulness, Flow,Enjoyment and Behavioral Intention (to return to NTU Co-Space). The evaluative results show that the fusion ART-based agents deployed in E3 are more useful in assistingthe subjects and E3 is significantly better in Telepresenceand Perceived Interactivity than those of the other twoenvironments. Moreover, the Flow experience perceived bythe subjects in E3 (i.e., populated with the fusion ART-basedvirtual humans) is significantly stronger than that in E2 (withthe AIML based virtual humans).

II. RELATED WORK

A. Modelling Virtual HumansOver the past 15 years, a number of research groups

have explored different aspects in modelling realistic virtualhumans, including autonomous behaviors, interactive non-verbal and verbal behaviors and agents with personality andmood. Table I provides a summary of some representativesystems in view of the three criteria, namely autonomy,interactivity and personality.

Different branches of works have been conducted basedon various considerations. With the objective of implement-ing autonomous virtual humans, Merrick and Maher [11]created motivated reinforcement learning agent based vir-tual villagers in Second Life (SL). These agents exploretheir environment and learn new behaviors in response tointeresting experiences, so that they could adapt and evolveover time. In Edward’s work [13], agent based virtualworkers simulate human’s cognitive processes in operatingin dangerous situations at a virtual site named SEVESO.

Another important branch of works is building interactivevirtual humans in terms of verbal behaviors and non-verbalbehaviors. Laura [6], initially designed to investigate human-computer relationships, is capable of carrying out conversa-tions with players based on scripts and simple mechanismsfor remembering past events with players. Max [7] is aconversational virtual agent which has been developed as aguide to the HNF museum. By applying the goal-driven BDI

architecture, he is able to interact with visitors and providethem with information. Kenny et al. [10] developed a virtualpatient named Justina who is able to act and carry on adialogue like a real patient with a specific mental conditionof interest.

In view of the limitations of monotonous interactionbetween virtual humans and real humans, many researchershave started to incorporate personality into virtual humansto make them more human-like. Valerie [8] was designedto help the visitors in many ways such as giving directionsand checking weather forecast. Her facial expressions wouldchange according to her mood which varies at differenttimes. This creates a vivid feeling when visitors interact withher. Eva [2] was designed as a tutor of digital photography.It was developed based on a memory-based emotion modelthat used the memory of past interactions to build long-term relationships between the virtual character ”Eva” andplayers.

The works described above have developed a wide rangeof virtual agents with different features. Our work reportedin this paper is one of the few that have developed virtualhuman agents that exhibit autonomy, interactivity and per-sonifications in real time to make virtual human agents asrealistic as possible.B. Evaluative Studies

Although many approaches have been proposed for build-ing human-like agents in virtual worlds, there have beenrelatively few studies in investigating the effect of virtualhumans on user experience. We briefly review relevant workbelow.

Within Second Life, in the ancient City of Uruk 3000B.C., virtual agent based non-player characters (NPCs) serveas the knowledge carriers as well as an important elementin establishing the connection between the environment,objects and knowledge [12]. In social simulations, studieshave shown that synthetic characters have provided an en-gaging and believable experience for the trainees in trainingleadership and negotiation tactics as well as in creatingmore dramatically interesting game situations [1]. Besidessocial simulations, virtual patients for the assessment andtraining of novice clinicians have been evaluated as well.The evaluation shows that they can provide valid, reliableand applicable representations of live patients [9]. Furtherevaluation also indicates that the interactions between noviceclinicians and the virtual patients resulted in a compatibledialectic in terms of rapport, discussion of the traumaticevent, and the experience of intrusive recollections [10].

Beyond the computer science field, some work has alsobeen done in the domain of information systems. Chaturvediet al. [14] have examined the design, development, valida-tion, and use of virtual worlds, especially those using agent-based simulation as the underlying technology. They derivea set of design principles based on deep versus emergentstructures while developing extended design principles to

Table ICOMPARISON OF THE REPRESENTATIVE VIRTUAL HUMANS.

Affiliation Application Autonomy Interactivity PersonalityLaura [6] MIT Real-Estate Agent No Yes NoMax [7] Bielefeld U. Museum guide No Yes NoValerie [8] CMU RoboCeptionist No Yes YesMRL [11] U. of Sydney Villagers Yes No NoJustina [10] USC Virtual Patient No Yes NoMASVERP [13] UTC Virtual operators Yes No NoEva [2] U. of Geneva Geography teacher No Yes Yes

facilitate their integration. Nowak and Biocca [24] con-ducted an experiment to examine the influence of agentson presence, copresence, and social presence in a virtualenvironment. The result shows that people respond sociallyto both humans and agents. Barfield and Hendrix [15]found that agents shaking continuously or appearing to movebetween large discrete positions will likewise diminish aparticipant’s sense of presence.

III. THE INTEGRATED AGENT MODEL

Using fusion ART [4] as a building block, our proposedagent is designed to fulfil the critical requirement of auton-omy, interactivity and personification. As shown in Figure 1,the integrated agent model consists of a Perception Modulereceiving situational signals from the environment througha set of sensory Application Programming Interfaces (APIs)and an Action Module for performing actions through thevarious actuator APIs. If the sensory signals involve a textinput, the Chat Understanding Module interprets the text forthe player’s intention. The outputs of Situational Assessmentand Chat Understanding Modules thus serve as part ofthe working memory content. The Inference Engine thenidentifies the most appropriate action, by tapping a diversepool of knowledge, in accordance to the desire, intention andpersonality of the virtual agent. The knowledge learned andused by the Inference Engine include declarative knowledgeof self, players, and environment, as well as proceduralknowledge of goal-oriented rules, which guide an agentin fulfilling goals, and social rules, for generating sociallyappropriate behavior. The decision of the Inference Engineagain forms part of the Working Memory, which maintainsthe context of the interaction. For actions involving a verbalresponse, the Natural Language Generation Module trans-lates the chosen response into natural text for presentation.

Working cooperatively, the various components describedabove give rise to the three key characteristics of virtualhumans, namely autonomy, interactivity, and personification,described as follows.Autonomy Based on a family of self-organizing neural mod-els known as fusion Adaptive Resonance Theory (ART) [4],the Inference Engine of the proposed agent architecture per-forms a myriad of cognitive functions, including recognition,prediction and learning, in response to a continual stream ofinput signals received from multiple pattern channels. Asa result, an agent makes decisions not only based on the

Figure 1. A schematic of the integrated agent model.

situational factors perceived from the environment but alsoon her mental states characterized by desire, intention andpersonality. By modelling the internal states of individualagents explicitly, the virtual humans can live a more com-plete and realistic life in the virtual world.Interactivity For interaction between the agents and theplayers, an intuitive user interface is provided, through whicha player may ask typical questions and provide quick re-sponses by button clicks. The player may also enter free-textsentences via the chat box. The dual communication modeprovides the players both ease of use and flexibility. Whileinteracting with player, the agent builds an internal modelof the player, with his/her profile, interests and preferences.The player model in turn allows the agent to make intelligentconversations on topics relevant to the player.Personification For improving the believability of virtualhumans, our agents adopt the Five Factor Model (FFM) [16],which characterizes personality in five trait dimensions. Bygiving a weightage to each dimension, a unique personalitycan be formed by a combination of the traits. Compar-ing with traditional pattern-matching-based conversationalagent, our agents with strong openness and extroversionpersonality are much more warm and friendly as they do notstay idle and wait for input queries. Acting pro-actively, theyapproach the players, offer help, and initiate conversations.

IV. NTU CO-SPACE

The NTU Co-Space [17] is implemented using theUnity3D [18], a 3D game engine that can be deployed intodifferent platforms including Microsoft WindowsTM, Apple

Mac OSTM, and mobile devices. In our implementation,the Co-Space is embedded into a web page that can beeasily accessed using typical web browsers such as Inter-net ExplorerTM, Google ChromeTM, SafariTMand MozillaFirefoxTM.

The overall architecture of the NTU Co-Space is depictedin Figure 2. At the back-end, two application servers,Apache TomcatTMand SmartFoxTM, support the multi-userenvironment. A dedicated database is used for the storageand retrieval of environmental and content data.

Figure 2. System architecture of NTU Co-Space.

As illustrated in Figure 3, a player can roam aroundthe virtual NTU campus on his/her own for self-discoveryor interact with autonomous virtual humans or other play-ers. Besides showcasing the intelligent agent technolo-gies, the NTU Co-Space can be played by any prospec-tive students and general public who are keen to knowNTU. A video clip of the NTU Co-Space can be viewedon YouTube (http://www.youtube.com/watch?v=bYIthOYjrxw).During the user trial, multiple players will be able to login and experience the NTU Co-Space concurrently.

Figure 3. A player touring the NTU Co-Space.

A. Agent InstantiationWe have built several virtual human characters, each with

a different profile and behaviour. Our main virtual agent,named David, has a set of four personal goals: (1) To stayhealthy, (2) To graduate with a degree, (3) To make friendsand (4) To stay wealthy. Following the Five Factor Model(FFM), he has an open and extrovert personality.Knowledge of Self: Besides the desire, intention and per-sonality, a virtual agent should be aware of its identity,background, and current activities. Such knowledge of selfis represented using the Concept-Relation-Concept (CRC)tuples [20], which are generic in form and easily expandable.A sample set of the agent self-knowledge is shown inFigure 4.

Figure 4. An example of agent profile model.

Knowledge of Environment: Inhabiting in the virtual en-vironment, a virtual agent should have the knowledge andsituational awareness of its surrounding. A sample set ofknowledge about the NTU Co-Space, also in the form ofCRC tuples, is given in Table II.

Table IIEXAMPLES OF KNOWLEDGE OF CAMPUS IN THE FORM OF CRC TUPLES.

Knowledge of Campus(Campus, Name, NTU)(Campus, Location, Western part of country)(Campus, Number of colleges, 4)

Knowledge of Others: In a virtual world, virtual humanslive alongside with other virtual humans and human avatars.As such, knowing other people is also key to interactingwith the others. Specifically, by remembering the profileand activities of the human players, virtual agents willbe able to interact with players in a more intelligent andnatural manner [19]. Likewise, knowledge of others can berepresented as CRC tuples.

B. Agent Embodiment

The input state of the agent consists of three categories ofcontext factors: Environmental context, virtual human con-text and player context. The environmental context consistsof global factors, such as time and location. The virtualhuman context comprises six types of attributes, includingdesire, personality, intention, previous performed action,natural language understanding and self model. The playercontext includes the player model and attributes such as,

whether the player is approaching, whether the player startsa conversation, and whether the player requests something.A summary of these attributes together with the possiblevalues is given in Table III. All attributes adopt a binaryencoding scheme in the state vector.

Table IIIATTRIBUTES AND POSSIBLE VALUES IN THE STATE SPACE.

Attributes ValuesTime Morning, Lunch time, Afternoon, Dinner

time, Evening, Bed timeLocation LT, Canteen, Shop, Hall, Library, Sports

facility, Schools, Admin building, Banks,Other buildings

Agent’s Approach, Guide a tour, Follow schedule,Previous Action Wandering, Start a conversation, End the

conversation, Greeting, Talk about player’sinterest, Talk about campus, Offer help toplayer, Describe nearby landmark, Askwhat player means, Ask what player wantto know/do, Rephrase the question, Replyplayer’s request, Provide suggestion,Continue conversation

Player Verbal Input Type, Subject, Object, Modifier, VerbPlayer New/old player, Personality, Interest,Model Interaction preference, GoalAgent Personality, Interest,Model Interaction preference, GoalPlayer Activity Wandering, Approach virtual human, Start

conversation, Make request, Followingguidance, Respond to virtual human, Givefeedback to virtual human

The virtual humans are designed to inhabit in the virtualworld, behave autonomously, and interact with players andother virtual humans. The action field thus consists of threecategories, namely chat related behaviors, social behaviorand other behaviors. A summary of these attributes togetherwith the possible values is given in Table IV.

Table IVCATEGORIES AND ATTRIBUTES IN THE ACTION SPACE.

Categories AttributesNon-Verbal Approach, Guide a tour,Behavior Follow schedule, WanderingVerbal Start conversation, End conversation,Behavior Greeting, Offer help to player, Talk about

campus, Talk about player’s interest,Describe nearby landmark, Ask what playermeans, Ask what player want to know/do,Continue conversation, Rephrase question,Reply player’s request, Provide suggestion

For interacting with the players, the virtual agent is initial-ized with a set of basic interaction rules and social rules. Byusing fusion ART as the inference engine, the agent is ableto operate based on inserted rules and create additional ruleswhen necessary using reinforcement learning. Illustrativesamples of the interaction and the social rules are given inTable V and Table VI respectively.

V. EVALUATIVE EXPERIMENTSA. Research Methodology

We developed three versions of NTU Co-Space, each witha distinct type of virtual agents in the form of Non-Player

Table VA SAMPLE INTERACTION RULE.

IF Personality is AgreeableAND Player asks for guidance to XAND No immediate planned activityTHEN Guide player to X

Table VIA SAMPLE SOCIAL RULE.

IF Personality is AgreeableAND Player initiates conversationAND Distance is farTHEN Walk towards the player

Characters (NPCs). The first environment (E1) provides thebaseline control condition, wherein the NPCs are only ableto display static messages but do not have the capabilityto interact with the users. The second environment (E2) isthe first treatment condition, wherein the virtual humans aredesigned as embodied conversational agents using the Ar-tificial Intelligence Mark-Up Language (AIML) [5]. AIMLis an XML-compliant language that was developed by theAlicebot free software community. It is considered as a rulebased repository of knowledge where the engine performspattern matching to select the most appropriate output basedon the input utterance. We have encoded as many AIMLpatterns as possible to enhance the conversational abilitiesof the agents. The third environment (E3) is the secondtreatment condition, wherein autonomous agents using ourproposed fusion ART-based agent model are populated.Although the agents we described in the previous sectionshave different personalities, for the purpose of this study,we remove the variation in personality by deploying onlyfriendly agents.

Subjects were randomly assigned to one of the threeconditions. The scenario given to the subjects is that theywere looking for an overseas university for an exchangeprogram and were visiting NTU Co-Space to help them inmaking the decision. Each subject was asked to completea quest in the form of a mini-game, where they wouldexperience the key places of the NTU campus throughthe quest. The quest involves finding five check-points oncampus where the clue to each check-point was given at theprevious check-point. To maintain information equivalenceacross the three environments, an interactive navigation map,containing essential information of the campus, is providedto the players in all three conditions.

The objectives of the experiment are two-fold. First, wewant to observe whether deploying virtual humans in thevirtual world will benefit the player’s experience. Second,we assess how virtual humans with different levels ofintelligence may affect the player’s experience, especiallyin terms of the following constructs, namely Telepresence(TP), Social Presence (SP), Perceived Interactivity (PI),Perceived Usefulness (PU), Flow (FLW), Enjoyment (ENJ)and Behavioral Intention (to return to NTU Co-Space) (BI).Research procedures: Subjects participated in the experi-

ment in a computer lab. They were provided with a set ofdetailed instructions on the experimental procedures. Beforethe experiment began, the experimenter conducted a shorttutorial session to familiarize the subjects with the basicsof the environment and how to perform basic functionssuch as walking, talking and using maps in NTU Co-Space. After the tutorial, the subjects were asked to fillout a pre-questionnaire and then carry out the experimentby completing the quest given to them. For subjects in theE1 (control) condition, they completed the quest by usingthe map in the system to navigate the virtual world, checkup information on different parts of campus and teleportto the respective checkpoints without receiving any helpfrom NPCs. For subjects in the E2 (i.e., first treatment)condition, they were not only provided with the interactivemap in the E1 condition, but they could also talk to theembodied conversational agents to ask for assistance beforeteleporting through the interactive map. For subjects in theE3 (i.e., second treatment) condition, in addition to beingprovided with the interactive map, they were also offered theassistance of fusion ART-based NPCs that have the abilityof performing autonomous behaviors both in proactive andresponsive ways; moreover, since these NPCs are embeddedwith a Natural Language Processing module, they can under-stand input sentences in a flexible way. Hence, subjects wereable to interact with the intelligent autonomous NPCs torequest for and obtain the information they needed. Becausethe NPCs are autonomous, they could even offer teleportservice to the specific locations requested. After the subjectshave completed the quest, they filled out a post-questionnairewhich assessed their experience. This questionnaire capturedvarious aspects of user experience which would be discussedin the latter part of the paper.Subjects: Subjects were recruited from an Introduction toManagement Information Systems (MIS) class at a largeMidwestern US university. In line with the institutionalboard review requirements, participation in this study isvoluntary and will be compensated with extra credit forthe course. Altogether, 58 subjects participated in this study.Specifically, 18 subjects were randomly assigned to E1, 20subjects to E2 and 20 subjects to E3.Pre-Questionnaire: Before the subjects started playing theAmazing Quest, a pre-questionnaire was used to assessthe players’ profile, covering demographics information,computer experience (internet, gaming, 3D virtual world),immersive tendencies, and 3D virtual world skills. Exceptfor demographics information, all factors were measuredusing a five point Likert scale, from 1 (not at all) to 5 (to avery great extent).Post-Questionnaire: After the subjects finished playingthe game, another questionnaire was issued to them. Thesubjects were first asked for the acronym followed by thefull name of the university to assess their level of recall. Themain part of the questionnaire then captured the subjects’

assessment of the seven constructs (as described earlier)related to NPC functions. In addition, 3D virtual world skillswas also captured to examine their perceived improvementof skills after experiencing NTU Co-Space.

Among the various constructs, Perceived Usefulness couldbe objectively assessed through the time taken to completethe Amazing Quest. The less time spent to complete theAmazing Quest, the more useful the agents are. Flow wascaptured through providing the description of Flow, and thenasking subjects to rate the degree of Flow they experiencedand the frequency in which they experienced Flow. Theother constructs were captured using measurement items.At least five items were used to measure each construct.The items used to measure Telepresence, Enjoyment andBehavioral Intention were derived from Nah [21]. Thescale for measuring Social Presence and Interactivity wereadopted from Animesh et al [22]. The items were capturedusing a seven point Likert scale where 1 refers to stronglydisagree and 7 refers to strongly agree. Given the limitedspace, we present a sample set of these items in Table VII.

Table VIIA SAMPLE SET OF POST-QUESTIONNAIRE.

Item MeasurementTP 1 I forgot about my immediate surroundings

when I was navigating in the virtual world.SP 1 During the virtual tour, the interaction

with the virtual humans were warm.ENJ 1 I found the virtual tour to be fulfilling.BI 1 I would consider visiting this virtual

world site again.

B. Data Analysis

Overall performance: Table VIII shows the overall perfor-mance in the three environments based on whether subjectscompleted the quest, the time taken to complete the questand whether the subjects could recall the acronym of thevirtual campus. We observe that subjects in all the threeenvironments were able to complete the quest successfully.However, subjects in E3 spent the least amount of time andthe percentage of subjects who could correctly recall theacronym of the campus is higher than those of the othertwo environments. This indicates that the autonomous agentsdeployed in E3 are more useful in helping the subjects thanthose of the other two environments.

Table VIIIOVERALL PERFORMANCE

Evaluation Measures E1 E2 E3% of players complete the quest 100% 100% 100%Time to complete the quest 20m31s 25m32s 16m56s% of Players recall the acronymof the campus 44% 25% 45%

3D virtual world skill: Table IX shows the 3D virtual worldskill before and after subjects experienced NTU Co-Space inthe three environments, where SD means STANDARD DE-VIATION and CI represents CONFIDENCE INTERVAL. Itwas captured using a seven point Likert scale, where 1 refersto strongly disagree and 7 refers to strongly agree.

Table IX3D VIRTUAL WORLD SKILL

Environment Measurement Before After DifferenceE1 Mean 3.61 4.33 0.72

SD 0.54 0.55 0.01CI 3.08-4.14 3.79-4.87

E2 Mean 3.93 5.03 1.10SD 0.43 0.14 -0.29CI 3.51-4.34 4.88-5.17

E3 Mean 3.50 4.66 1.16SD 0.60 0.33 -0.27CI 2.92-4.08 4.33-4.99

Table IX indicates that in E2, subjects perceived highestlevels of 3D virtual world skill both before and after ex-periencing NTU Co-Space. However, in E3, the subjects’skill have been increased the most compared with the othertwo conditions. The results on confidence intervals suggestthat with a confidence level of 95%, E2 has the highestrating of skills before experiencing NTU Co-Space whilethat of E3 is the lowest. After experiencing NTU Co-Space,subjects in E2 still have the highest rating of skills whereassubjects in E3 perceived higher skill than those in the controlcondition (E1). This indicates that with the interactionwith autonomous agents, users feel more comfortable andperceived themselves to be more skillful with the virtualworlds.Descriptive Statistics: Table X shows the means, standarddeviations (SD), and confidence intervals (CI, with a confi-dence level of 95%) of ratings in E1, E2, and E3 in termsof Telepresence (TP), Social Presence (SP), Perceived Inter-activity (PI), Enjoyment (ENJ), Flow (FLW) and BehaviorIntention (BI). All of the constructs were measured usingthe seven point Likert scale. From the table, we observethat for E3, the rating of Telepresence, Social Presence,Perceived Interactivity, Flow and Behavior Intention havebetter results than E1 and E2. This means by employingautonomous agents, these factors are perceived to be strongerthan those in the environment with dummy and AIML basedagents. However, we note that for enjoyment, the rating in E2is the best of the three environments. Referring to Table IX,subjects in E2 appear to have the best 3D virtual world skillscompared to the other two. As prior work have shown thata higher level of skill is likely to enhance the feeling ofenjoyment [23], we believe the rating obtained in E2 couldbe affected by the higher level of 3D virtual world skills.

Furthermore, a one-way analysis of variance (ANOVA) isused to analyze the results. Specifically, the F-test is usedto evaluate the hypothesis of whether there are significantdifferences among the statistic data means for those con-structs. The F values are calculated by the variances betweenconditions divided by the variance within the conditions.The p values, on the other hand, represent the probabilityof test statistic being different from the expected valuesand are directly derived from the F test. A small p valuethus indicates a high confidence that the values of those

constructs are different. A summary of the F values andp values among E1, E2 and E3, between E1 and E3, andbetween E2 and E3 are given in Table XI.

Table XIF-TEST RESULT

E1, E2 & E3 E1 & E3 E2 & E3Constructs F p F p F pTP 5.47 0.004 11.22 0.001 4.55 0.034SP 0.58 0.561 1.10 0.295 0.01 0.903PI 5.71 0.004 4.00 0.047 12.12 0.001ENJ 0.15 0.862 0.33 0.566 0.17 0.68FLW 1.62 0.199 0.28 0.595 3.12 0.079BI 1.23 0.294 2.40 0.120 0.10 0.751

This data analysis revealed the significant effects of thethree kinds of virtual humans in virtual worlds on Telep-resence and Perceived Interactivity: F (2, 519) = 5.47, p <0.01 for Telepresence and F (2, 345) = 5.71, p < 0.01 forPerceived Interactivity, where the two parameters enclosedin parentheses after F indicate the degrees of freedom ofthe variances between and within conditions respectively.Consistent with the statistics in Table X, the fusion ART-based virtual human generate higher levels of Telepresenceand Perceived Interactivity than the other two types of virtualhumans, with a mean of 4.41 for E3 (versus 3.86 for E1 and4.08 for E2) for Telepresence, and a mean of 5.02 for E3(versus 4.66 for E1 and 4.37 for E2) for Perceived Interactiv-ity. Although the effect of E1, E2 and E3 on Flow is smallerthan that of Telepresence and Perceived Interactivity, thedifference in Flow is perceived to be marginally significantbetween E2 and E3, with F (1, 198) = 3.12, p < 0.1, and amean of 4.58 for E3 (versus 4.21 for E2). This means theFlow experience perceived by subjects who interacted withthe fusion ART-based virtual human is stronger than thosewho interacted with the AIML based virtual humans. Nosignificant difference was found for the rest of the constructs.

VI. CONCLUSION

We have built and deployed realistic agents in an inter-active 3D virtual environment known as NTU Co-Space.With the virtual characters befriending and providing per-sonalized context-aware services, we hope players will findvirtual worlds more fun and appealing. To the best of ourknowledge, this is one of the few in-depth works on buildingrealistic agents in virtual worlds with autonomous behavior,natural interactivity and personification.

More importantly, we have carried out a systematic ex-perimental study to assess whether the use of agents-basedvirtual humans can improve user experience in the virtualworlds. Our study has so far mostly supported the validityof our agent systems. Moving forward, we shall extend ourstudies to agents with different personalities. Specifically, wewould like to assess whether the resultant more dynamic vir-tual environment would offer players a different experience.In terms of agent architectures, we wish to develop morecomplete cognitive architectures that incorporate emotion

Table XDESCRIPTIVE STATISTICS

E1 E2 E3Constructs Mean SD CI Mean SD CI Mean SD CITP 3.86 0.34 3.64-4.08 4.08 0.49 3.76-4.40 4.41 0.42 4.14-4.68SP 3.63 0.44 3.28-3.98 3.82 0.43 3.47-4.16 3.84 0.65 3.32-4.36PI 4.66 0.67 4.13-5.19 4.37 0.58 3.90-4.83 5.02 0.66 4.49-5.54ENJ 4.42 0.44 4.04-4.81 4.50 0.33 4.11-4.90 4.31 0.72 3.68-4.94FLW 4.47 0.54 4.00-4.94 4.21 0.28 3.96-4.46 4.58 0.68 3.98-5.18BI 4.23 0.71 3.61-4.56 4.51 0.30 4.24-4.78 4.58 0.40 4.23-4.93

modelling and expressions as well. Since the emotion modelmay influence the decision making process and providethe attentional mechanism which can also help in naturallanguage processing, it could have a broader impact on boththe affective and cognitive functions of virtual humans.

ACKNOWLEDGEMENT

This research is supported by the Singapore NationalResearch Foundation under its Interactive & Digital Media(IDM) Public Sector R&D Funding Initiative and adminis-tered by the IDM Programme Office.

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