Crowded Collaborative Virtual Environments

8/3/2019 Crowded Collaborative Virtual Environments

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PAPERS CHI 97 * 22-27 MARCH 1997The remainder of this paper is structured as follows. Thefollowing section considers the motivations forintroducing an explicit crowd mechanism into CVES. Wethen introduce the underlying mechanism of third partyobjects as an extension to the spatial model. Followingthis, we discuss how third party objects can be used tocreate a variety of different kinds of crowds in CVE3.Finally, we present a demonstration application,implemented using the MASSIVE-2 system, called theArena, which combines static and dynamic crowds with astructured space so as to create a venue for on-lineperformances.MOTIVATIONS FOR A CROWD MECHANISMWe begin by considering the motivations for introducingan explicit crowd mechanism into CVEs (i.e. for providingan additional level of technical support for crowdrepresentation and management beyond just the ability forlarge numbers of individual participants to gather in oneplace).Our fmt motivation is scale. Current CVEs support atmost a few tens of simultaneous users, Although there aresome exceptions, such as the NPSNET battle simulatorwhich claims of the order of a hundred simultaneousparticipants [4], these have generally only been achievedby relying on then being highly predictable behaviorsfor objects (e.g. the movements of ships, tanks andmissiIes) and by reducing the potential for communicationbetween participants (especially with regard to real-timeaudio). There are severrd dimensions to the problem ofscale. First, can the network exchange rich informationabout many simultaneous participants sufficiently quicklyand reliably so as to engender a sense of co-presence?This is a major limitation for systems based on unicastnetwork protocols but will eventually become an issueeven for systems which utilise more network efficientmukicast protocols. Second, assuming that the networkcan deliver this information, can the computers involvedprocess and render it? Third, even if the combination ofnetwork and computer can deliver and display theinformation, can individual participants make sense of it?(e.g., could one make sense of a thousand people speakingat once or view a thousand detailed embodiments at thesame time?).This leads us to our second major motivation, that oflegibility and structure. Complex environments might bemade manageable for users by introducing additionalstructures which group objeets together, provide ag~gateviews of them and which might then be unfolded at a latertime (e.g. on entering them). Some initiat evidence for thisis provided by recent work on enhancing the legibility ofinformation visualisations through the introduction ofdistricts and related features such as landmarks, edges,paths and nodes [3].

Our final motivation involves generating a sense of mass-presence for specific classes of application. Audiencesplay a key role in various real-world events such a theatre,concerts, sports, exhibitions, fairgrounds, trade shows,rallies, demonstrations and even town centres and publicspaces. A crowd mechanism might therefore enhance oreven create a sense of audience presence andparticipation in CVB and might open up opportunities fornew forms of social interaction.THIRD PARTY OBJECTS AND AWARENESSOur framework for realising crowds in CVEs is based onour previous spatial model of interaction and in particular,on a recent extension to the model called third partyobjects. The spatial model defines mechanisms for themanagement of awareness and communication in sharedvirtual spaces [2]. To briefly summarise, the modelconsiders a number of objects in a shared virtual spacecommunicating through different media (e.g. audio,graphics, text and video). Instead of having each objecttransmit its information to all other objects, basicconnectivity is enabled through the concept of aura - avolume of space that delimits the presence of an object ina given medium. Thus, aura collisions lead to connectionsbeing established. The quality of any information which issubsequently transmitted (e.g. the volume of audio or thelevel of detail of graphics) depends upon the level ofawareness that the observer has of the observed(awareness is a quantifiable concept in the model). This inturn is negotiated through fcms and nimbus. Focus is asub-space representing the attention of the observer andnimbus is a sub-space representing the projection ofinformation by the observed. The observers awareness ofthe observed is then some function of the observers focuson the observed and the observeds nimbus on theobserver, Aura, focus and nimbus may be mediumspecific, multi-valued, dynamically changeable and neednot h strictly spatial in their definition (i.e. they need notbe simple discrete volumes of space).This basic model is limited in two main ways: its hi-lateralapproach to interaction does not easily scale to largenumbers of participants and, beyond a limited concept ofadapter objects, it provides no support for introducingcontextual factors into awareness negotiations (e.g. forrepresenting the effects of the environment within whichthe observer and observed find themselves). Third partyobjects have been introduced in order to address theseproblems. A third party object is an independent objectwhich affects the awareness between other objects. Thebasic scenario (in any given medium) is therefore now oneof three objects, each with individual awarenessrelationships to the others (see figure. 1).

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CHI 97 * 22-27 PV!AW:H 1[>(~ PAPERS

Figure 1: Introducing third party objectsThree general points should be noted about third partyobjects from the outset. FirsL all aspects of their operationas described below may be medium specific (e.g. theymay operate differently in the audio medium than in thegraphical, textuaf or video media). Second, as they areobjects in their own right they may be embodied, mobileor fixe4 dynamically or statically created and may applytheir effem recursively to one another. Third, althoughthey are most often described in spatiat terms in thispaper, they may operate according to non-spatialawareness relationships (i.e. one could define them interms of arbitrary attributes of objects).There are three key aspects to third party objects: theireffects (i.e. what they do to awareness relationshipsbetween other objects), their activation (i.e. when and howthese effects are brought into operation) and their creationand destruction (i.e. how they are introduced to andremoved from the environment). We now consider each ofthese.The effects of third party objectsThird party objects can have two general kinds of effecton awareness (see figure 2) which may be applied indifferent combinations across different communicationmedia. 635secondary sourcingA Badaptation

Figure 2: the effects of third party objectsAduprafion involves the manipulation of existingawareness relationships between objects. In this sense,third party objects are a generalised notion of the adaptersthat were defined in the initial spatial model. Thesemanipulations include attenuation (e.g. a bamier betweenobjects) and amplification (e.g. increasing awarenessbetween people who are accessing a common object).Secmda~ sourcing involves the introduction of newindirect awareness relationships between objects in order

to enable new transformed flows of information betweenthem. Typically, secondary sourcing involves theconsumption of information from an external group ofobjects, its transformation in some way and its subsequentre-transmission in order to provide a common view of thegroup. Various filters may also be applied at differentstages of this process in order to reduce level of detail orto select key information. At the heart of secondarysourcing lies the problem of creating a single aggregateview or stream of information from a number of sources.We propose that there are three approaches to theaggregation problem:q selection - switching between individual views or

streams in some way (e.g. round robin, loudest winsetc.)

cornbifwtion - the direct composition of a new view fromexisting views (e.g. tiling multiple video windows).

abstraction - generating an entirely new representationbased on statistical information describing the sources(e.g. mapping the number of sources into the size of therepresentation, the level of their activity into COIOLUetc.).

We will provide concrete examples of these classes oftransformation when specifically discussing crowds lateron.The activation of third party objectsNext we consider the circumstances under which differentcombinations of these effeCLs are applied. l%e activationof third party objects is bassed on the awarenessrelationships between the third p,arty and the other objectsinvolved. Thus, referring to figure 1, the activation of Tdepends on four possible awareness relationships: Tsawareness of A and B respectively and their awareness ofit. In figure 3 we identify three particularly interestingcases from among the various possibilities.Gf$fi!

)mwnhcr.ship h) sharing C) hybridFigure 3: Activating third party objects

u) mmhmhip - cases where the third party is activatedaccording to how aware it is of other objects. This isanalogous to the idea of membership (i.e. the third partysawareness of an object expresses the degree ofmembership of that object). For example, one mightbecome a member of a room by crossing its boundary.b) sharing - cases where the third party is activatedaccording to how aware other objects are of it. This is

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PAPERS CHI 97 * 22-27 MARCH 19analogous to the idea of objects sharing the third party insome way and consequently, it having an effect on theirmutual awarenessc) hybrid - cases where the effects of the third partydepend on how much one object is aware of it and howmuch it is aware of another object. This turns out to be auseful case for crowds (see below).Craation and daatruetion of third party objectsGiven that they are independent objects in their own right,third party objects might be created and destroyed in anyof the ways associated with normal objects. Thus, theymight be static {i.e. a permanent part of a givenenvironment) or dynamic (i.e. created or destroyed on thefly). It is also necessary to consider the issue of whocreates and destroys them. Again, there are three cases toconsidecq [he system - third parties might be automatically created

or destroyed by the CVE operating system according tonetwork and system load factors.

q the application developer - might act as an architect,defining the structure of an environment through thirdparty objects in order to afford different modes ofparticipation in a specific event. Thus, in this case,different third parties might be associated with differentarchitectural units or features (e.g. making use of alibrary of different boundary types).

s Ihe end user - end users might request the creation anddestruction of third party objects themselves (e.g.dynamically creating a private sub-discussion).

Exampias of third patty objaetsBefore going on to consider how they can support theintroduction of crowds into CVES, we fmt briefly list anumber of representative broader applications of the thirdparty mechanism:q bounded rooms and buildings - the creation of

membership activated regions of space whoseboundaries might have different effects across differentmedia and which might be hierarchically combined toform arbitrarily complex virtual buildings.

q jloor control objects - membership based third partieswhich attenuate direct awareness between members andreplace it with a secondary source view based on anappropriate selection algorithm. common foci - objects which would amplify awarenessbetween people who were using them (e.g., shareddesigns or information items in a shared visualisation).

q group vehicles - mobile third parties which would becapable of steering a group of people through a worldand which would provide a shared environment forexperiencing it.

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load management groups - a possibly invisible systemlevel third party used to dampen down system load (i.e.reduce network traffic by replacing many individualawareness relationships with a single aggregate view).data districts - a cluster of data in an informationvisualization that would be seen as an aggregate from adistance but could be unfolded if required. The districtcould also provide awareness of the presence of otherindividuals within that district.

This concludes our general introduction to third partyobjects in the spatial model. The following section nowconsiders how they may be used to support crowds.CROWDS AS THiRD PARTY OBJECTSCrowds can be realised as a specific class of third partyobject which support potentially large groups of people(and possibly agents and other objects) in CVES. We nowconsider the following aspects of crowds as third partyobjecw effects on awareness, representation, activationand membership, creation and destruction, mobility andgenemtion and behaviour.Effaeta on awaraness?We propose that, in general, crowds should have anasymmetric effect on awareness.From the outside, such as when perceived fmm adistance or from the perspective of a non-member,individuals within the crowd are hidden (adaptation ofexisting awareness) and, instead, are replaced with anaggregate view of the whole crowd (secondary sourcing),On the inside, individuals are able to interact with eachother in the normal way (i.e. through their respective fociand nimbi). Typically, they will atso be aware of thoseoutside of the crowd on an individual basis. Indeed, insome cases the crowds may even amplify the awarenessthat those inside have of those outside such as in the casewhere people in an audience wish to be maximally awareof the performers at an event. Thus as a member of acrowd, I can communicate with nearby people who arealso in the crowd, and can perceive those outside of thecrowd in detail, although they may only perceive me in asmuch as I contribute to the aggregate view of the crowd.Of course, given that they are third party objects, crowdsmay contain other crowds, thereby applying these effectsrecursively to one another.Crowd aggravationsDeveloping appropriate aggregation techniques is clearly acritical issue for building useful and convincing crowds.Although the details of particular techniques will beapplication specific and are therefore beyond the scope ofthis paper, this section does propose an initialclassification of approaches according to the twodimensions of general approach (i.e., selection,

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CHI 97 * 22-27 MARCH 1997 PAPERScombination or abstraction as identified above) and themedium involved.I I select I combine I abstract Igraphics

audio

floorcontrol/

schedulingpoliciesas above

combineinto

hierarchicalobject

multiplex?

text as above digestifying

visualisestats (e.g.Paradise)audiomixingautomaticabstractingmorphing?counting(e.g.&141Stadium)

A summary of crowd aggregation techniquesEach entry in the above table refers to a possibletechnique for aggregating many sources in a givenmedium into a single output. This aggregate may then befiltered and translated to create a final representation insome (possibly other) medium. Note that we have definedan additional medium called events, which coversapplication defined events and protocols mnging from thegeneral presence and location of objects through tospecific events such as pressing an applaud now buttonon a user interface.Several of the entries in this table suggest the applicationof well known techniques. For example, selection acrossany of the media could utilise a range of floor control andscheduling algorithms such as round robin, randomselection, most active, currently active and so forth. Itmay also be possible to adapt existing text manipulationtechniques for the text medium, including digestifying dsused on newsgroups and automatic abstracting andindexing. Combination in the graphical medium mightinvolve the creation of a new super-object whose parts aredefined by the individual sources. Although not inherentlyscaleable in itself, this approach could be combined withautomatic level of detail techniques. Video tiling providesa way of combining multiple video views into a singleview, although this approach would appear less scaleable.Two current systems can be associated with two of theentries. The Paradise project has been exploring the use ofgraphical abstraction techniques to produce aggregateviews of groups of objects for use in distributed simulation[6]. Their approach generates statistics about graphicalobjects (e.g., the number present, their mean location andspread) and these aggregations are then used to generategraphical representations. Alternatively, the KM stadium,developed by the UKs Open University, uses a thresholdtechnique where a sufficient frequency of applause events

coming from different participants triggers a crowdapplause event (which is eventually translated into theplay back of an audio sample). This approach could alsodrive the playback of video samples or graphicalanimations.Two further entries in table are worthy of special note asthey would appear to pose great difficulties. These are thecombination of audio signals and the abstraction of videosignals. In the everyday world one never perceives acombination of audio signals that are not alreadysuperimposed. Other than having two ears (which allowssome spatial separation), there is therefore no general wayof distinguishing an individual audio signal from among acombination (especially a large combination). Conversely,although one might somehow use morphing techniques toblend video images together, in the real world one rarelyperceives superimposed visual images (with the limitedexception of partially transparent surfaces) and so, ashumans, we have no apparatus for usefully dealing with anabstraction of visual signals. In other words, we suspectthat the nature of our audio and visual perception willmake the development of useful audio combination andvideo abstraction techniques especially difficult.Aetlvation and membershipWe identify two general styles of activating crowds. Firstis a class of crowd whose effects are solely triggered bymembership (i.e. on the crowds awareness of otherobjects - figure 3 case a). Thus, the crowd can determinewhich objects are members and which are not andtypically operates such that: members are normally awareof both members and non-members; non-members arenormally aware of non-members; but that non-membersare only aware of members through an aggregate view.Membership might be directly mapped onto spatialattributes such as proximity (i.e. one becomes a memberof a crowd by crossing its boundary), but could potentiallyinvolve other non-spatial attributes. Our second class ofcrowd is based on the hybrid approach (figure 3- c). Thisoperates as for the membership based example with onekey difference. Whether a non-member perceivesindividual members or not depends on how aware they areof the crowd. This allows people outside of the crowd tounfold it just by looking at it hard enough, even if theyare not themselves members.In essence, both classes of crowd use the idea ofmembership to determine whether or not an objectcontributes to the aggregate view. The difference is thebasis on which that view is perceived by others -according to how aware they are of the crowd or viceversa.It should also be noted that, just as awareness ispotentially a multi-valued quantity in the spatial model, sothen is the idea of membership. One can extend these

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PAPERS CHI 97 * 22-27 MARCH 199examples to include multiple levels of membership whichactivate different combinations of effects.MobilityCrowds might be fixed or mobile objects in a virtualenvironment. Fixed crowds might be attached to variousfeatures of an environment such as a bank of seating in anauditorium. Mobile crowds introduce the further issue ofthe relationship between crowd and individual navigation.There are several possibilities here: crowds might follow their members in some way (e.g.

remaining at their mean position).q crowds might navigate on behalf of their members (e.g.

group vehicles which take many people on a sharedride).

s a combination of these where both the crowd and itsmembers exert a pull on one another (i.e. where thecrowd would follow its members but also slow themdown if they moved too far away).

Craation and daattuctionCrowds can be created and destroyed at all three of thelevels identified previously, by the system, applicationdevelopers and the participants themselves. However, thefmt two are of particular interest.The system might automatically introduce crowd objectsinto an environment in order to manage system load byreducing the number of individual awareness relationshipsto be considered. This might be done to handie a suddenmass of new arrivals into an environment. Our experienceswith the MASSIVE system suggest that people often moveto new places together (e.g. a group of people might leavean environment together at the end of an event) and thatsuch movements can cause intense bursts of networktraffic (movements need to be conveyed to other objects,descriptions of new worlds need to be transferred acrossthe network and so forth). The temporary introduction of acrowd object might help smooth this process and thecrowd could then be removed once the major movementshad settled down and a new phase of activity wasunderway.Adopting a longer term view, the structure of a virtualenvironment might be used to predict where crowd objectscould usefully be located. For example, in a persistentenvironment such as a virtual town, it may be useful toassociate a crowd with key locations such as squares,major pathways and junctions. Indeed, recent research intothe structure of virtual environment inspired by urbanplanning theory has pointed towards there being a directcausal relationship between the structure of a virtualenvironment, the navigation strategies employed by itsinhabitants and the places where social encounters arelikely to occur [3]. In short, given knowledge of theformer two, it may be possible to predict the latter. Such

knowledge would suggest in advance where crowd objectsmight most beneficially be introduced into theenvironment.Application developers might introduce crowds in theform of different architectural features in the design of avirtual environment in order to pm-configure thecommunications that might eccur within it (e.g. as a bankof seating in a auditorium, a hallway in a building etc.).This suggests the use of our framework to create a libraryof architectural components with associated crowdproperties.Agents, generation and behaviourAs a final note, it may be useful to be able toautomatically generate or simulate crowds for virtualenvironments, even where there are relatively few humanparticipants. One approach to this might be the use ofautonomous agents who join crowds and carry out simpleactions in response to an event, nearby humans or eveneach other. Indeed, the use of such agents is going to beessential for initial testing of systems (see below). Theautomatic generation of crowds raises the question ofwhether it might be possible to introduce, control or re-inforce crowd behaviors among human participants. Thissuggests combining the kind of crowd mechanismsproposed in this paper with other crowd simulationtechniques (e.g. [5]).THE ARENA - A DEMONSTRATION OF CROWDSIn this section we present a prototype application of ourframework. This prototype, called the Arena, realises avirtual space for on-line performance to a live audience. Inthis case, the performance is a simple interactive ballgame between several participants. The Arenademonstrates the foliowing features of our frameworkq the use of third party objects to support crowds. example graphics and audio aggregation techniques.q fixed and mobile crowds.q examples of crowds activated solely on the basis of

membership and also level of detail crowds whoseeffects depend on an observers awareness of them.

q hierarchical combination of several crowds with abounded space so as to create a virtual space whichoffers its inhabitants different modes of participationdepending upon where they are located within it.

q the use of agents which exhibit simple behaviors inorder to generate crowds for testing purposes.

MASSiVE-2The Arena has been implemented using the MASSIVE-2system, a general purpose CVE which supports theextended spatial model of interaction and provides aplatform for creating different kinds of third party object

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CHI 97 * 22-27 MARCH 1997 PAPERS[1]. Like its predecessor, MASSIVE, the system allowsusers to interact using graphics, text and audio media.M.ASSIV&2 relies heavily on the use of multicastnetworking protocols in order to achieve efficientnetworking. Objects which are members of a third party(e.g. the members of a crowd) send information to one ormore multicast groups associated with that third party.New objects which become members of the third party areautomatically invited to join this multicast group. Afurther mukicast group then allows new observing objectsto request a state snapshot from all of the transmittingobjects (i.e. there is a back-channel which allows arrivingobjects to request to catch up with the current state ofplay).As third party objects move around an environment sothey may recursively swallow each other up to form amobile hierarchy of spatial regions, crowds and otherkinds of group (e.g. crowds may enter rooms, vehiclesmay pass through regions etc.). Beneath the surface, this ismapped onto a dynamically evolving hierarchy ofmulticast groups, It is this highly dynamic use of multicastthat allows MASSIV132 to achieve both scalability andflexibility.Imptamentatlon of the ArenaWe now describe the implementation of the Arena as anapplication of MASSIVE-2. The Arena combines severalkinds of third party objecc a bounded space and two kindsof crowd, static apd mobile. The overall design of theArena is shown in figure 4.

0 = third party q = individualobject participant

Bo-&ded qF ~crowdspace

n qG

Figure 4 Overview of the ArenaThe Arem is housed in a bounded space - a static,graphically embodied third party object whose effects areto completely attenuate awareness between members andnon-members. Membership is achieved simply by crossingits boundary. Thus, those on the outside (participants F

and G in figure 4) cannot hear or see what is happening onthe inside and vice versa.Within the Arena space there are two further third partyobjects, both of which are static crowds. These are used tolocate the opposing supporters (the Reds and the Blues).They are membership based crowds which are createdwhen the application is initialised and whose position isfixed. They support two kinds of aggregation algorithm:q in the graphical medium, each crowd provides an

aggregate embodiment whose size increases accordingto the (square root) of the number of its currentmembers. Thus, as more people enter the crowd, so thesize of its aggregate view increases. Our current crowdembodiment is similar in appearance to that of theindividual participants so that the crowd takes on theappearance of a giant sized person.

q in the audio medium, each crowd sums the audioemissions of its members (i.e. mixes them together) andapplies a low pass filter so as to distinguish the tone ofthe crowd from the tone of individual speakers.

The area between the static crowds represents theperformance space. For the performance we have createda simple ball game (similar to the classic computer gamePong) where several participants bat a graphical ballbackward and forwards over a net. Of course, they cantalk to each other as well.The presence of the two crowds inside of the Arena spacegives rise to several different modes of awareness betweenits inhabitants. Referring to figure 4, C and D, who are inthe same crowd, have normal mutual awareness of oneanother, have full awareness of the performers, B and A,but only perceive E in as much as they contribute to theircrowd aggregation. As performers, A and B have fullmutual awareness but only perceive the audiencememlxxs, C, D and E through their respective crowdaggregations.One or more dynamic crowds can be created outside of theArena in order to handle the sudden outflow ofparticipants at the end of the event. Although they use thesame aggregation techniques, these differ from the staticcrowds inside the Arena in several respeck.q

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q

they are mobile such that, at any given time, theyassume the mean position and orientation of theirmembers.they can be dynamically introduced or just left waitingaround outside of the Arena ready to pick up passersby.they work as level of detail crowds - i.e. whether theaggregate view of individual members are perceived bya non-member depends upon their level of awareness ofthe crowd. Thus, even at a distance, one can unfold thesecrowds by raising ones focus sufficiently.

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PAPERS CH 197 + 22-27 MAKi+ 19In order to test and demonstrate this application we havealso implemented some simple agent based crowdmembers who may occupy the Arena along side its humanparticipants. These have been given the ability to followvarious pre-defined paths through the environment (e.g.they can be sent off into or out of the Arena as a group)and also voice some simple chants (i.e. play back pre-recorded audio samples). It should be noted that, from anetwork point of view, these Agents operate just as humanparticipants (i.e. they are separate entities in their ownright who generate traffic according to their actions).Figure 5 shows how the Arena appears from within theperformance space and includes two red players, two blueplayers, the red and blue crowds (viewed as threeaggregates and one unfolded aggregate) and a scoreboard.Figure 6 shows how the Arena appears to a member of theblue crowd. In this case, we can see several nearbyindividual members of the blue crowd, the performers anda secondary source view of one of the more distant redcrowd aggregates.SUMMARYOur paper has been concerned with supporting crowds ofparticipants in collaborative virtual environments.Specifically, we have introduced a framework forreasoning about and developing different kinds of crowdswith different effects on spatial awareness andcommunication. This framework is based on an extensionof our previous spatial model of interaction called thirdparty objects. The key points of our framework are:q crowds can play a significant role in configuring

communication and awareness. In particular, they mayintroduce aggregate views of their members.

q the use of awareness to activate crowds supports crowdswhose effects are based on membership and also on anobservers level of focus.

q crowds may be mobile or fixed and may be created anddestroyed at the system level, the applicationdevelopment level or by participants themselves.

We have also presented a demonstration of our frameworkbased around an application catled the Arena - a virtualenvironment for staging different kinds of performance infront of an audience - which has been implemented usingthe MASSIVE-2 platform.Having established this general framework, furtherresearch is now required. First, research is needed intoalternative and more powerful aggregation techniques fordifferent communication media. Second, it may be

beneficial to create a library of higher level buildingblocks for creating different kinds of crowds. Such alibrary might represent a set of standard architecturalbuilding blocks for virtual worlds that could easily beaccessed by application developers without the need forextensive programming. Third, trials are needed withsignificant numbers of participants (possibly combinedwith agents) in order to assess both the human and systemimplications of this approach (e.g. under whatcircumstances do people experience a sense of crowdpresence and what is the impact on networking andcomputation). Finally, greater consideration needs to begiven as to how this kind of framework might be realisedusing future public delivery platforms (e.g. a combinationof cable to the home linked to so called set-top boxes orgames consoles). Given that these various issues can beaddressed, we anticipate that our framework may becomea significant component in constructing mass participationsocial electronic environments and that, in the future, suchtechnology could have widespread applications in manyareas of life including arts, entertainment, leisure andculture.REFERENCES1

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Greenhalgh, C. M. and Benford, S. D., IntroducingRegions into Collaborative Virtual Environments,Internal report available from the authors of thispaper (submitted to IEEE VRAIS97).Greenhalgh, C. M. and Benford, S. D., MASSIVE AVirtual Reality System for Tele-conferencing, ACMTransactions on Computer Human Interaction(TOCHI), 2 (3), pp. 239-261, ACM preSS, 1995.Ingram, R. J., Bowers, J. M. and Benford, S. D.,Building Virtual Cities: Applying Urban PlanningTheory to the Design of Virtual Environments, Proc.VRST96, Hong Kong, July 1996, ACM Press.Macedonia, M. R., Zyda, M. J., Pratt, D. R., Barham,P. T. and Zeswitz, S., NPSNET: a network softwarearchitecture for large scale virtual environments,Presence, 3(4), MIT Press, 1994.Sime, J. D., Crowd Psychology and Engineering:Designing for People or Ballbearings? EngineeringFor Crowd Safety (Smith R.A and Dickie J.F., eds),ppl 19-131. Elsevier 1993.Singhal, S. K. and Cheriton, D. R., Using ProjectionAlgorithms to Support Scalability in DistributedSimulation, Proc. 1996 International Conference onDistributed Computing Systems, IEEE, 1996.

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Crowded Collaborative Virtual Environments