In vivo, in vitro, in silico, in virtuo · In vivo, in vitro, in silico, in virtuo, 1st Workshop on SMA in Biology at meso or macroscopic scales, Paris, july 2, 2008. 3 Perception

Tisseau J., In vivo, in vitro, in silico, in virtuo, 1st Workshop on SMA in Biology at meso or macroscopic scales, Paris, july 2, 2008. 1

In vivo, in vitro, in silico, in virtuoJacques TISSEAU

UEB-ENIB, LISyC, Brest UniversityEuropean Center for Virtual Reality

CERV, 25 rue Claude Chappe, 29280, Plouzane, [email protected]

1 IntroductionComplex systems are everywhere, whether in biology - the greatest source of inspiration - or in physics, dataprocessing or economics. New analytical methods are vital if we want to study these systems in detail withoutrelying on simplifications which can misrepresent or distort them. [...] To study complex systems, we could tryto apply conventional analytical methods: writing up a set of equations which are supposed to describe the overallbehavior of the system, and solving those equations. But generally speaking, we don’t know how to write them.

Zwirn H., La complexite, science du XXIeme siecle ?, [Zwirn 03]

The systems we want to model are increasingly complex. This complexity is essentially due to the diversityof components, the diversity of structures and the diversity of interactions put into play. Therefore,a complex system is, on the face of it, an environment which is open (components appear/disappeardynamically), heterogeneous (varied behaviors and morphologies) and made up of mobile compositeentities distributed over space and in varying numbers over time. These components, amongst whichhumans and their free will often play a determining role, can be structured into di!erent levels, eitherknown from the start or emerging as they evolve due to multiple interactions between the components.The interactions themselves may be of di!erent types and operate on di!erent spatial and time scales.But as the quotation heading this section suggests, no theory is currently capable of formalizing thiscomplexity, and in fact there are no a priori methods of formal proof like those which exist in highlyformalized models. Without such formal proof, we must fall back on experimenting on a system as itdevelops, in order to be able to experimentally validate it afterwards. Today, virtual reality provides aconceptual, methodological and experimental framework which is well adapted to imagining, modelingand experimenting on this complexity.

Virtual reality is a scientific and technical field using information technology and behavioral interfaces to simulatethe behavior of 3D entities in a virtual world. They interact with each other and with one or more users in realtime, through pseudo-natural immersion via sensory motor channels.

Fuchs P., Arnaldi B., Tisseau J., La realite virtuelle et ses applications, [Fuchs et al. 03]

According to the definition above, derived from VR work carried out by the French scientific community,virtual reality simulation therefore allows true interaction with the modeled system. Like a biologistperforming in vitro experiments, this simulation lets us observe phenomena as if we had a virtual mi-croscope which can be moved and oriented as we wish, with a choice of focal points. The cre-actor user(spectator-actor-creator) can thus focus on observing a specific type of behavior, a subsystem’s activityor the overall activity of the system. The user can interrupt the phenomenon at any time, accuratelyfocus on the bodies present and the interactions underway, and then restart the simulation where it wasstopped. At any time, by using sensory-motor behavioral interfaces, the user can disturb the system bymodifying the property (status, behavior) of an element or by removing elements or adding new ones.This means the user can test a specific behavior, or more generally speaking, an idea, and immediatelyobserve the consequences on the system under operation. Virtual reality puts the user at the heart of thevirtual laboratory, bringing him closer to the methods of experimental science while providing access todigital methods.

In virtuo (in the virtual) is a newly coined expression constructed by analogy to adverbial phrases from Latinsuch as in vivo (in the living) and in vitro (in glass). Biologists often use the expression in silico (in silicon) todescribe computer calculations, however, in silico fails to conjure up human participation in the world of digitalmodels being run: which is why we prefer the expression in virtuo, whose common root provides a reminder ofthe experimental conditions of virtual reality.

Tisseau J., Realite virtuelle : autonomie in virtuo, [Tisseau 01]

Going beyond simply observing a digital model being performed on a computer, the user can test itsreactivity and adaptability while it is running, thus taking advantage of the behavioral nature of digitalmodels. We call this new type of experiment in virtuo experimentation. An in virtuo experiment is one


carried out in a virtual world of interacting digital models in which a human being is taking part. Virtualreality fully involves the user in the simulation, which is closely akin to the participatory design approach[Schuler et al. 93], preferring to consider users as human actors rather than human factors [Bannon 91].In VR, this sort of participatory simulation implements various types of models (multi-model) fromdi!erent fields of expertise (multidisciplinary). It is often complex, since its overall behavior dependsas much on the behavior of the models themselves as on the interactions between models. Lastly, itmust include the free will of the human user making use of the online models. In virtuo experimentationalso involves an experience which just analyzing digital results does not provide. Between a priori formalproof and a posteriori validations, there is room today for virtual reality experienced by the user, who canthus go beyond generally accepted ideas to ideas grounded in experience. We suggest that this in virtuoexperimentation be placed at the heart of the 21st century laboratory, so that the inherent complexityof systems, whether natural or artificial, can be made intelligible.

Microscope, telescope: these words evoke the great scientific penetrations of the infinitely small and the infinitelygreat. [...] Today, we are confronted with another infinite: the infinitely complex. And this time we have noinstrument to use. We have only our brain - our intelligence and our reason - to attack the immense complexityof life and society. [...] We need, then, a new instrument. [...] I shall call this instrument the macroscope (frommacro, great, and skopein, to observe).The macroscope is unlike other tools. None of that has occurred by chance.It is a symbolic instrument made of a number of methods and techniques borrowed from very di!erent disciplines.It would be useless to search for it in laboratories and research centers, yet countless people use it today in themost varied fields. The macroscope can be considered the symbol of a new way of seeing, understanding andacting.

De Rosnay J., Le macroscope : vers une vision globale, [De Rosnay 75]

Although modeled systems are increasingly complex, the formalism which could truly describe theircomplexity is still lacking today. Only virtual reality enables this complexity to be experienced. Therefore,we must further explore the relations between virtual reality and theories of complexity, so that virtualreality becomes an instrument to investigate complexity, as in the “macroscope” imagined by Joel deRosnay in the 1970s. But we prefer the term of “virtuoscope” to macrosope, since it reminds us thatthese systems are studied, first and foremost, through the models we make of them and experiment on inour virtual laboratories. In the long term, the virtuoscope project should provide scientists from all fieldswith methods and instruments to study complex systems within virtual laboratories by implementing thein virtuo experiments that VR can provide. To support this flagship project, we shall begin by draftingthe foundations of this virtual laboratory of the 21st century, taking virtual reality as a base to buildon (Section 2). We can then use this new construction’s epistemological position to shed new light onthe now-classical methods of scientific reasoning (Section 3). Finally, we will highlight the main stakes,whether scientific, methodological, technological or societal (Section 4).

2 The virtuoscope

The virtuoscope will provide onlineuse of digital models, based on VRconcepts, models and tools (Section2.1). Virtual reality is based on twoprinciples, i.e., presence and auton-omy (Section 2.2). Implementationof these two principles is made pos-sible by making models autonomous(Section 2.3) in the IT frameworkof multi-agent systems. The latter’ssimulations become participatory byfully involving users and their freewill.

2.1 Making use of models

Models mainly operate through the three modes of perception, experimentation and modification. Eachprovides a di!erent way of mediating reality [Tisseau et al. 98b].


Perception of a model : The model is perceived through sensory mediation: the model’s activity isobserved through all of the user’s senses. The same is true for spectators in a dynamic cinematheater, watching a hemispheric screen with a sound-surround system from a seat mounted onhydraulic or pneumatic jacks, who really feels as if they are taking part and immersed in theanimated film they are watching, even though they cannot modify its course. The quality ofsensorial renderings and their synchronization is essential here, since this is a field where real-timeanimation excels. The most widely accepted definition of ”to animate” is to put into movement. Inthe more specific field of animated film, to animate means to give the impression of movement byrunning through an ordered collection of images in (drawings, photos, computer-generated images,etc.). These images are produced by applying a model for object movement in the scene shown.

Experimenting on the model : Experimentation on the model brings mediation of the action intoplay: meaning that the user tests the model’s reactivity using the appropriate manipulators. It islike a fighter pilot using a flight simulator, whose training mainly focuses on learning how the aircraftis going to react. Based on the principle of action and reaction, here, the emphasis is on the time-based rendering’s quality: which is what interactive simulation does best. The standard meaningof simulation is to make something which is not real seem real. In scientific fields, simulation is anexperiment on a model. It can be used to test the quality and internal coherence of a model bycomparing its results to those obtained by experimenting on a modeled system. It is increasinglyimplemented today to study complex systems where humans play a role, used both to train operatorsand study users’ reactions. In these human-in-the-loop simulations, operators thus provide theirown behavioral model which can interact with the other models.

Modifying the model : Mental mediation means that the user modifies the model himself, with accessto the same level of expressiveness as the modeler. This also applies to an operator who partiallyreconfigures a system, while the rest of it remains operational. This is the fast growing field ofinteractive prototyping and online modeling, in which ease of intervention and ability to expressoneself are vital. To attain this level of expressiveness, the user generally has the same interfaces,and above all the same language, as the modeler at his disposal. Mediation of the mind thus is thusachieved through language mediation.

In this way, the related fields of real-time animation, interactive simulation and online modeling representthree aspects of model operation. The three of them provide the three-fold mediation of what is realwhich virtual reality requires. They define three levels of interactivity.

• Real-time animation is the baseline level of interactivity between the user and the model being run.The user is subjected to the model, since the user cannot act on any of its parameters, but is simplyits spectator.

• Interactive simulation is the first level of interactivity because the user has access to some of themodel’s parameters. Thus, the user plays the role of actor in the simulation.

• In on-line modeling, the models are themselves the parameters of the system: a higher level ofinteraction is reached. The user himself, by modifying the model as it runs, takes part in creatingthis model and thus becoming a cre-actor (creator-actor).

The user can interact with the image, using the appropriate behavioral interfaces. However, withinthe world of models, the user can only observe or do what the system controls through its peripheraldevice drivers providing the indispensable links between man and machine. This means that the sys-tem is in charge of the user’s sensory-motor mediation and that this mediation is modeled within thesystem in one way or another. The user’s only true freedom lies in his decision-related choices (mentalmediation) which are restricted by the system’s limitations in terms of observation and action. So takingaccount of a user must be made explicit by using a specific model of an avatar to represent him withinthe system. At the least, this avatar will be placed in the virtual environment in order to define the viewpoint required to render the various senses. It has virtual sensors and actuators (for vision [Renault 90]and hearing [Noser et Thalmann 95], and hand grips [Kallmann et Thalmann 99]) to interact with othermodels. The data collected by the avatar’s virtual sensors are transmitted in real time to the user by


the device drivers, while the user’s orders are sent in the opposite direction to the avatar’s actuators.There are also means of communication to communicate with other avatars, which thus reinforce thesensory-motor capacities by enabling it to receive and emit language-type data. There may be no vi-sualization whatsoever of the avatar (BrickNet [Singh 94]), or it may be limited to a simple, textured,but unstructured 3D primitive (MASSIVE [Benford 95]), part of a poly-articulated rigid-segment system(DIVE [Carlsson et Hagsand 93]), or a more realistic display which takes sophisticated behaviors likegestures and facial expressions into account (VLNET [Capin et al. 97]). When this display is available,it makes it easier to identify avatars and the non-verbal communication between them. Thanks to theuser’s explicit modeling, three main types of interaction can coexist with the digital model universe:

• model-to-model interactions like collisions or bonding;

• model-to-avatar interactions that enable sensory-motor mediation between a model and a user;

• avatar-to-avatar interactions which allow avatars to meet in a virtual environment shared by severalusers (televirtuality [Queau 93a]).

The user’s status in virtual reality is di!erent from his status in scientific simulations of numericalcomputations or interactive simulation using training simulators. In scientific simulation, the user setsthe model parameters beforehand, and interprets the results of the calculation afterwards. In the case ofscientific visualization system, he can observe how the computations change, possibly using VR sensorialdevices [Bryson 96], but remains enslaved to the model. Scientific simulation systems are model-centeredsystems, since science models seek to give reality universal representations which are distinct from in-dividual impressions. In this case, the user acts as a spectator. On the contrary, interactive simulationsystems are essentially user-centered, to give the user all the means he needs to control and pilot themodel: the model must remain the user’s slave. The user goes from simple spectator to actor. By intro-ducing the avatar concept, virtual reality places the user on the same conceptual level as the model. Thisreplaces the master-slave relationship by an equal-to-equal relationships and increased model autonomy,and consequently greater autonomy for the user. Through a three-fold mediation of senses, action andlanguage, the user becomes the true cre-actor of the models being run.

2.2 Presence and autonomyGeppetto took his tools and began to cut and shape the wood into a puppet. ”What shall I call him?” he said tohimself. ”I think I’ll call him Pinocchio.” [...] Having found a name for his puppet, he set to work in good earnestto make first his hair, then his forehead and then his eyes. The eyes being finished, image his astonishment whenGeppetto noticed that they moved and stared fixedly at him. [...] he then took the puppet under the arms andplaced him on the floor to teach him to walk. Pinocchio’s legs were so sti! that he could not move them, butGeppetto led him by the hand and showed him how to put one foot before the other. When his legs were morelimber, Pinocchio began to walk by himself and run around the room. He came to the open door and with oneleap, was out into the street and was gone.

Carlo Collodi, The Adventures of Pinocchio, [Collodi 1883]

Virtually reality has historically focused on the concept of the user being present inside virtual worlds[Tisseau et Nedelec 03]. Robotics experts were already using terms such as telepresence [Sheridan 87],telesymbiosis [Vertut et Coi!et 85] or even tele-existence [Tachi et al. 89], to describe the impression anoperator can have of being immersed, the impression of being present in the place where the robotis working, although he is manipulating it remotely [Johnsen et Corliss 71, Minsky 80]. So the firstVR studies were naturally turned toward designing and creating behavioral interfaces which favoredthe immersion of the user and his abilities of interaction in a virtual universe [Fuchs et al. 03]. Theseinterfaces can characterize the presence of the user inside virtual worlds [Slater et al. 94, Schloerb 95,Witmer et Singer 98, Morineau 00] and shed light on the philosophical reflections about this feeling ofubiquity [Flach et Holden 98, Zahorik et Jenison 98].

To the notion of the user’s presence, we’ve added the notion of autonomy of the models which make upand structure the virtual universe [Tisseau 01]. An object’s behavior will be considered as autonomous,it if can adapt to unknown changes in its environment: meaning it must have the means to perceive, actand coordinate perceptions and actions, to be able to react realistically to these changes. This notionof autonomy is essential to combine the behavioral rendering required for VR with the multi-sensoryrendering of graphic data. In fact, on a daily basis, we run up against a reality which confronts and


resists us and is governed by its own laws and not ours, which is, in a word, autonomous. Virtual realityis freed from its origins by moving towards making the digital models it manipulates autonomous, inorder to populate the realistic universes that computer-generated images already let us see, hear andtouch, with autonomous entities. Thus, VR is refocused on the digital models it uses just as much as onthe behavioral interfaces needed to use them [Tisseau et al. 98b].

So, we can characterize a virtual reality application according to these two criteria of presence andautonomy, with presence being characterized by criteria of immersion and interaction. In this way, anapplication can be represented by a point in a 3D reference scheme (Figure 1): immersion / interaction /autonomy, with standardized axes between 0 (criterion entirely absent) and 1 (criterion entirely present).Within this immersion / interaction / autonomy reference, 3D cinema (1,0,0) corresponds to a typical im-mersion application, and a video game (0,1,0) to a typical interactive application, while a flight simulator(1,1,0) o!ers the user both immersion and interaction. A computer virus (0,0,1) is a typical example ofan autonomous application which escapes his creator but is not, however, controlled by the user. Virtualtheater (1,0,1) lets a user be immersed as an observer, free to move about in a scene played by autonomousvirtual actors, but unable to influence their behaviors; on the contrary, interactive fiction (0,1,1) allowsa non-immersed user to interact with autonomous actors. So a typical VR application (1,1,1) allows animmersed user to interact with autonomous virtual entities; hence, the user of this sort of applicationtakes full part in the artificial life in these realistic universes made up of autonomous models.

immersion

interactionvideo game

3D cinema

computer virus

presence

autonomy

virtual theatre

interactive fiction

virtual reality

flight simulator

Figure 1: Presence and autonomy in virtual reality

This conception of virtual reality ties in with Collodi’s old dream. The latter, as we saw in thequotation at the beginning of this section 2.2 made his famous puppet an autonomous entity fulfilling thelife of his creator. The steps Geppetto took to reach his goal were the same as those we have observed invirtual reality. He began by identifying him (I’ll call him Pinocchio), then turned to his appearance ([he]started by making his hair, then his forehead) and then made him sensors and actuators (then his eyes[...]). Next, he defined his behaviors Geppetto led him by the hand and showed him how to put one footbefore the other) to make him autonomous (Pinocchio began to walk by himself) and finally, he couldonly see that making a model autonomous leads to the creator’s losing control of his model (he leapedinto the street and was gone).

In this way, just like the famous Italian puppet (Figure 2), models which have become autonomoustake part in inventing their virtual worlds. When humans are freed in part from controlling their models,they will gain in autonomy themselves, participating in this virtual reality as spectator (observing themodel’s activity), actor (experimenting on the model by testing its reactivity) and creator (modifyingthe model to adapt it to their needs by defining how proactive it is).

2.3 Making models autonomousVirtual objects, just like the space they are in, are actors, and agents. Endowed with a memory, they have func-tions to process information and an autonomy which is regulated by their programs. Virtual worlds are constantlybeing crossed by a strange, intermediate, artificial life. Each entity, each object and each agent can assimilated toan expert system with its own rules of behavior, which it applies or adapts in response to environmental changesor modifications in the rules or metarules which govern the virtual world.

Queau P., Le virtuel, vertus et vertiges, [Queau 93b]

Enabling a model’s autonomy means giving it the means for perception and action inside its own en-vironment, as well as a decision making module letting it adapt its reactions to stimuli which can be


a. user-spectator b. user-actor c. user-creatorPinocchio, model of the Child, can also be used as a metaphor to describe the three dimensions ofvirtual reality.

immersion The Italian puppet is a physical model: so the user-spectator has no di!culty inimmersing himself in his world (since he is made of the very wood we use for firewood).

interaction The puppet’s designer provided the appropriate manipulator to control its movement.Thus, thanks to the puppeteer’s control bar, the user-actor can interact with the puppetand test its reactivity.

autonomy The user-creator has modified the model by making it autonomous.

Figure 2: Pinocchio metaphor and virtual reality

external or internal. In rendering models autonomous, we are guided by three points: autonomy inessence, autonomy through necessity and autonomy due to ignorance.

Autonomy in essence characterizes living organisms, from a cell to a human being. Avatars are notthe only models which can perceive and act within their digital environments: any model whichis supposed to represent a living being must be given this sort of sensory-motor interface. Ani-mats, for instance, are artificial animals whose laws of functioning are inspired by those of animals[Wilson 85]. Like an avatar, an animat is located in an environment; it has sensors to acquireinformation and e!ectors enabling it to act within this environment. Unlike an avatar which mustbe controlled by a human user, an animat must control itself in order to coordinate its perceptionsand actions [Meyer et Guillot 91]. This control can be innate, i.e. pre-programmed [Beer 90], butwill more often be acquired in the animat approach, in order to simulate how behaviors adaptedfor survival in changing environments come about. Therefore, research in this very active field1

[Guillot et Meyer 00] mainly concerns the study of learning (epigenesis) [Barto et Sutton 81], de-velopment (ontogenesis) [Kodjabachian et Meyer 98] and evolution (phylogenesis) [Cli! et al. 93] ofthe control architecture. Animating the virtual creatures obtained using these di!erent approachesprovides a highly illustrative example of these adaptive behaviors [Sims 94]. Modeling virtual ac-tors falls under the same approach [Thalmann 96]. Thus, by rendering a model associated with anorganism autonomous, we can more faithfully account for the autonomy observed in the organismin question.

Autonomy through necessity involves instantly taking changes in the environment into account byorganisms or mechanisms alike. Physically modeling mechanisms usually depends on solving dif-ferential equation systems. Solving them requires knowledge about the boundary conditions whichlimit movement; and yet, in reality, these conditions can change constantly, whether or not thecauses are known (interactions, disturbances, modifications in the environment). Therefore, themodel must be capable of perceiving the changes in order to adapt its behavior while running.This is all the truer when humans are present in the system, since, though their avatars, they canprovoke changes that were entirely unforeseeable at the outset. The example of sand in an hourglasscan illustrate this point. The physical simulation of granular media is usually based on microme-chanical interactions between spheres of varying hardness. To visualize flows of about one secondtakes several hours of computation, making these simulations unsuitable for virtual reality con-straints [Herrmann et Luding 98]. Modeling using larger grains (mesoscopic level) based on specific

1From Animals to Animats (Simulation of Adaptive Behavior: www.adaptive-behavior.org/conf): biennial conferencesheld since 1990.


masses which are linked to each other through the appropriate interactions, leads to satisfactory,but not interactive, visualizations [Luciani 00]. Another approach takes separate, large grains ofsand, which individually detect collisions (elastic impacts) and are gravity-sensitive (free fall). Itcan simulate the flow of sand in the hourglass, but also adapt in real time to the hourglass beingturned over, or a hole being created in it [Harrouet 00]. Thus, rendering any model autonomouswill enable it to react to unforeseen situations appearing as it runs, and which are due to changesin the environment caused by the activity of other models.

Autonomy due to ignorance reveals our present inability to describe the behavior of complex systemsusing the reductionistic methods of the analytical approach. A complex system is an open systemmade up of a heterogeneous group of atomic or composite entities. The behavior of the group is theresult of the individual behavior of these entities and their various interactions within an environ-ment which is also active. Depending on the school of thought, the group’s behavior is consideredeither as being organized with respect to an aim, called teleological behavior [Le Moigne 77], oras the result of the system’s self-organization, which would be called emergence [Morin 77]. Thelack of models for the overall behavior of complex systems leads to control being distributed at thesystem component level and thus, making the components’ models autonomous. In this case, thesimultaneous evolution of these components provides better understanding of the overall system’sbehavior. Thus, a set of autonomous models interacting inside the same space contributes to thestudy and experimentation of complex systems.

Making models autonomous, whether in essence, though necessity or from ignorance, helps populatevirtual environments with an artificial life which reinforces the impression of reality. The following threemodes are used to make entities autonomous: sensory-motor, decisional and operational modes. This ise!ectively based on sensory-motor autonomy, since each entity has sensors and e!ectors which enableit to be informed about and act on its environment. It also relies on decisional autonomy, since eachentity decides according to its own personality (background, intentions, state and perceptions). Finally,it requires autonomy of performance: the controller of each entity’s operation is independent from thecontrollers of the other entities.

In fact, the notion of autonomous entity overlaps that of an agent in the individual-centered ap-proach of multi-agent systems. The first studies on multi-agent systems were carried out in the 1980’s.They were based on the asynchronous aspect of actors’ language interactions in distributed artificialintelligence [Hewitt 77], and on the individual-centered approach of artificial life [Langton 86], as wellas on the autonomy of mobile robots [Brooks 86]. Two major trends are currently structuring thisfield of study, i.e., either attention is focused on agents as such (intelligent, rational or cognitive agents[Wooldridge et al. 95]), or the interactions between agents and collective aspects take precedence (re-active agents and multi-agent systems [Ferber 95]). This means we can find agents that reason on thebasis of beliefs, desires or intentions, called BDI, [George! et Lansky 87]), more emotional agents as insome video games (Creatures [Grand et Cli 98]), or purely reactive, stimulus/response type agents as ininsect societies (MANTA [Drogoul 93]). In any case, these systems di!er from symbolic planning modelsof classic Artificial Intelligence (STRIPS [Fikes et Nilsson 71]), since they accept that a sophisticatedbehavior can emerge from interactions between more reactive agents located in an active environment[Brooks 91].

3 An epistemological perspectiveCreated from two words with opposing meanings, the expression virtual realities is absurd. If someone were totalk to you about the color black-white, he would seem, combining one word with its opposite, to be confused aboutwhat he wanted to say. Of course, in the strictest sense, virtual and real are not opposites. The virtual, fromthe Latin virtus (virtue or force), is latent in reality, which contains all the necessary prerequisites for it to beachieved. But then, what could a reality containing all conditions for its achievement in itself possibly be? Fromthat point of view, the expression is even more inept.

Cadoz C., Les realites virtuelles, [Cadoz 94]

The emergence of the notion of virtual reality illustrates how dynamic interdisciplinary exchanges arebetween computer graphics, computer-aided design, simulation, teleoperations, audiovisual techniquesand telecommunications. . . [Tisseau et Nedelec 03]. But as the philosopher Gaston Bachelard stressed in


his epistemological study on the formation of the scientific mind [Bachelard 38], scientific or technologicalprogress must confront a number of epistemological obstacles. Amongst them, virtual reality will have toovercome at least the verbal obstacle (a false explanation obtained using an explanatory word), since itsname itself is meaningless at the outset, while referring to the intuitive notion of reality, one of the earliestand constituent notions of the human mind. The English expression virtual reality was first suggestedin July 1989 at a trade show2, by Jaron Lanier who was the head of the VPL Research firm, specializedin immersion peripheral devices at the time. He coined the term within his company’s advertising andmarketing strategy, without trying to provide a specific definition for it. According to the BBC EnglishDictionary (HaperCollins Publishers, 1992), Virtual: means that something is so nearly true that formost purposes it can be regarded as true, also means that something has all the e!ects and consequencesof a particular thing, but is not o"cially recognized as being that thing. Therefore, a virtual reality is aquasi-reality, which looks and behaves like a reality, but is not: it is more like a substitute or ersatz reality.The direct translation of this English term into French gives us “realite virtuelle”, which as emphasizedin the quotation at the beginning of this introduction, is an absurd and inept expression. Indeed, theFrench dictionary Le Petit Robert (Editions Le Robert, 1992), defines Virtuel: as having within itselfthe necessary prerequisites for its achievement. So virtual reality would be a reality containing all thenecessary conditions for its achievement, which is the least we’d expect from reality! We see that ingoing from English to French, the term of virtual reality has become ambiguous. There is a rhetoricalprocessed called an oxymoron, which consists in putting together two words which seem incompatibleor contradictory (expressions like living dead, clair obscur, or an eloquent silence are examples of this).This type of construction gives unexpectedness to an expression, which, we must agree, is more media-oriented than scientific. Other expressions like cyber-space [Gibson 84], artificial reality [Krueger 83],virtual environment [Ellis 91], or virtual world [Holloway 92], have also been put forward, but a rapidweb search indicates that the antonym virtual reality remains in widespread use. Another point of viewconsiders that reality is what exists in itself, independently of whether we can perceive it or see it or not(Dictionnaire historique de la langue francaise, Robert, 2000). This means that reality is a representationof what is real, i.e., a model - and virtual reality would therefore be a virtual representation, which is theleast we’d expect from a model! The term virtual reality then becomes a pleonasm [Mellet 04].

Between oxymoron and pleonasm, these ambiguities blur distinctions and create a true epistemologicalstumbling block for the scientific development of this new field of study. It is up to scientists andprofessionals in the field in question to make the e!ort of epistemological clarification, in order to removeambiguities and clarify its status as a scientific discipline (concepts, models, tools), especially with respectto closely related fields like modelling, simulation and animation. So a simulation typology must takethe new way of experimenting on models, which is in virtuo experimentation (Section 3.1) into account,as well as highlighting complementarities between the di!erent ways of modeling the same phenomenon(Section 3.2).

3.1 Types of simulationsBeyond the physical experiment lies another, which is abundantly practiced at a higher level - the thought experi-ment. The project inventor, the building of castles in the air, the novelist, the author of social or technical utopiaall experiment in their thoughts. But so do the down-to-earth merchant, the inventor or the serious researcher.They all think about the circumstances, and link a prudent approach and expectation to their perception; ie, theyrun a thought experiment. [...] Our representations are more easily and conveniently to hand than are physicalfacts. We experiment with thought, so to speak, at a lower cost. And so we mustn’t be surprised if the thoughtexperiment often precedes and prepares a physical experiment.

Mach E., Erkenntniss und Irrtum, [Mach 1905]

The main qualities of a model, i.e. an artificial representation of an object or a phenomenon, arebased on its abilities to describe, suggest, explain, predict and simulate. Simulating the model, orexperimenting on it, consists in testing the behavior of the representation under the e!ect of actionswhich can be exerted on the model. The simulation outcomes then become hypotheses that we try toverify by designing experiments on a single prototype of the real system. Thus rationalized, these aretruly in vivo experiments.

Traditionally, it is considered that there are four main kinds of models, i.e., perceptive, formal,analogical and digital models. Their experimentation has currently led to five main families of simulation:

2Texpo’89 in San Francisco (USA)


in petto intuition resulting from perception models, in abstracto reasoning within formal models, in vitroexperiments on analogical models, in silico computations on digital models, and now in virtuo experimentson these digital models.

In petto intuitions Simulation of a perceptive model correspond to in petto intuitions that spring fromour imagination and the sensory perception that we have of the system being studied. This enablesperceptions to be tested on the real system. Inspirations and associations of ideas and heuristics,neither codified nor reasoned, lead to the forming of mental images with a power of suggestion. Thescientific approach will try to rationalize these first impressions, whereas artistic creation will drawdigital or analogical works of art from them. But it is often the suggestive nature of the perceptivemodel which triggers creative moments which lead to an invention or discovery [Vidal 84], as statedby Alfred Wegener, the father of continental drift and seafloor spreading which gave rise to theplate tectonics theory in the late 1960s.

The first idea of continental translation symmetry came to mind in 1910. While looking at a map of theEarth, I was suddenly struck by the similarity of the coasts of the Atlantic, but I didn’t dwell on it at all atfirst, because I thought such translations were implausible.

Alfred Wegener, La genese des continents et des oceans, 1937

In abstracto reasoning The simulation of a formal model relies on in abstracto reasoning conductedwithin the framework of a theory. Reasoning supplies predictions which can be tested on the realsystem Galle’s discovery of the planet Neptune in 1846 using Adams and Le Verrier’s theoreticalpredictions illustrates this approach within the framework of the gravitational perturbation theorywith the two-body approximation in celestial mechanics. Likewise, in particle physics, the discoveryof the intermediate vector bosons W+, W? et Z0 in 1983 had been predicted a few years earlierby the theory of electroweak interactions. Hence, from the infinitely large to the infinitely small,the predictive nature of formal models has proved to be very fruitful in a large number of scientificfields.

In vitro experiments Simulation of an analogical model relies on in vitro experimentation on a sampleor mock-up which is built by analogy with the real system. Similarities between the mock-up andthe system improve the understanding of the system being studied. Aircraft mock-up trials in windtunnels enable aerodynamicists to better characterize the flow of air around obstacles, by studyingthe similarity coe"cients that Reynolds and Mach introduced at the end of the 19th century.Likewise, the analogy of the heart as a pump in physiology allowed Harvey to demonstrate thatblood circulation followed the laws of hydraulics (1628). Thus, the explanatory nature of analogicalmodels has always been used, more or less anthropocentrically to bring the unknown into the realmof the known.

In silico computations Simulation of a digital model means running a program which is supposed torepresent the system being modeled. In silico calculations provide results which are compared tomeasurements taken on the real system. Numerically solving mathematical equation systems is themost usual way of using digital modeling. Indeed, the analytical determination of solutions oftencomes up against di"culties which are due just as much to the characteristics of the equationsto be solved (non-linearity, matching) as to the complexity of boundary conditions and the needto take very di!erent space and time scales into account. Studying chemical reactions kinetics,calculating the deformation of a solid under thermo-mechanical constraints or characterizing anantenna’s electromagnetic radiation are classic examples of deploying di!erential equation systemson computers. Therefore, the digital model obtained by discretizing the theoretical model hasbecome a vital tool to surpass theoretical limitations, but is still often considered as a stopgapsolution.

In virtuo experimentation More recently, the possibility of interacting with a program being run hasopened a new path to in virtuo experimenting on digital models. It has become possible to per-turb a model while it is running, dynamically modify the boundary conditions, and eliminate oradd elements during the simulation. This gives digital models a virtual mock-up status, which isinfinitely more malleable than the real mock-up used in analogical modeling. Flight simulators orvideo games are forerunners of virtual reality systems which become necessary when recourse to


direct experimentation becomes di"cult or even impossible. This can be for various reasons: hostileenvironments, lack of accessibility, space-time, budget or ethical constraints. Thus, in virtuo exper-imenting now enables us to render certain of the thought experiments mentioned at the beginningof this section 3.1 in a tangible form, where until very recently, they were confined to imaginationalone.

In fact, these di!erent simulation modes are complementary and all or part of them can be imple-mented to appraise and understand a given phenomenon.

3.2 Modeling and simulation“We only reason on the basis of model” (P. Valery). But how do we develop the models we base our reasoningon? By models, we mean the intelligible artificial, symbolic representations of situations in which we intervene:modeling is both identifying and formulating a few problems by constructing their wording, and trying to solvethem by reasoning through simulations. By making the model-wording work, we try to produce models-solutions.Modeling and simulation, thought and reasoning, are the two inseparable sides to any deliberation.

Le Moigne J.L., La modelisation des sytemes complexes, [Le Moigne 90]

Whether we are literary or scientific minded, artists or engineers, study of an actual phenomenon beginswith our sensorial information (Figure 3). These impressions, confronted with our personal imagination,inspire us with in petto intuitions which are expressed as perceptions.

behaviors

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Figure 3: Phenomena modeling, simulation and understanding

Only in the second phase will the scientific approach seek to formalize these initial perceptions tocreate a representation as free of individual illusions as possible. In abstracto reasoning developed in theframework of an appropriate theory and which is usually based on a logical-deductive approach, will thuslead to predictions about the phenomenon being studied. In vivo experiments on the real system canalso be conducted to compare these predictions to experimental results.

But in many real situations, the formal approach alone, which is reductionistic in essence, cannotaccount for the complexity of the phenomenon studied. Therefore, we can have recourse to analogies


to carry out experiments on real mock-ups. These mock-ups or models can be obtained by scale analo-gies (like scale models) or by formal analogies (thermal ? electric type). Results from these in vitroexperiments will then be adapted to the real phenomenon by similarity (scale or conversion factors).

Today, recourse to digital methods and computer programs opens up another path for simulatinga formal model for which no analytical solution is available. Here we distinguish between the in silicocomputation and in virtuo experimentation by the absence or presence of a human in the simulationloop. In virtuo experiments allow the user to manipulate an actual virtual mock-up and to feel, or not,his first impressions, where in silico calculations would supply only numerical results.

In some cases, we try to use simulation to explain not a real phenomenon, but an idea. To materializehis idea, an artist goes directly from the perceptive model of his imagination to an analogical or digitalwork of art: this is the act of artistic creation which will be analogical or digital depending on themedium used. Whereas the engineer will go from the formal model of his theoretical framework to a realor virtual mock-up, i.e., the act of technological design made tangible in analog or digital form. Thismodel or mock-up, whether real or virtual, becomes a singular prototype for a new real phenomenonwhich can in turn be studied through new experiments. So the scientist enters an interactive processof modeling/simulation which enables him to clarify his idea and thus refine and improve the variousassociated models.

The notion of a model as a representation of reality is based on two metaphors, one of them artisticand the other legal. The legal metaphor of delegation (the elected o"cial represents the people, the papalnuncio represents the Pope, and the ambassador the Head of state) suggests the idea of replacement: themodel substitutes for reality. The artistic metaphor of realization (a play is given in public, artisticinspiration is represented by a work) suggests the idea of presence: the model is a reality. Consequently,while complementing our now classic means of investigation, i.e., in vivo and in vitro experimentation, orin silico computations, experimenting on a digital model in virtuo ensures it a true presence and opensnew fields of exploration, investigation and understanding of reality.

4 The stakesBetter to make use of the new possibility of standing back a bit from the intellectual imperialism of science, notto deny its importance or its interest or attempt to overshadow it, which is impossible, but on the contrary, totry at last to look at it from a distance at assign it its appropriate place in the cultural landscape. It would bepathetic to deny the e"cacy and scope of scientific knowledge, it would be absurd to refuse to use its instrumentsof thought. Even at that, we must decide what to do with it.

Levy-Leblond J.M., Aux contraires, [Levy-leblond 96]

For scientists, describing, explaining, predicting and simulating the behaviors of complex systems,whether natural or artificial, was the major challenge of the 21st century. Virtual reality will help meetthis challenge, whose related stakes are as scientific (Section 4.1), methodological (Section 4.2), andtechnological (Section 4.3) as they are societal (Section 4.4).

4.1 Scientific stakesModern science is pursuing the desire for knowledge which already existing in the Neolithic. It is obviously deeper,more extensive, because we have other concepts at our disposal, but the desire to know our surroundings is thesame. Previously, we thought that we Newton’s law and two or three others, we could understand it. Nature wasperceived as being fundamentally simple, whereas today, we see that it is fundamentally complex. [...] I rememberreading years ago a book by the American physicist Richard Feynman, called The Character of Physical Law.Feynman thought the world could be compared to a huge chess game, where complexity was only apparent andwhere each move was simple; once you knew the rules of the game, you could decipher the world. And yet, whatis more complex than a proton? A proton is made up of quarks and these quarks interact via gluons and all sortsof things. Nature has ceased to be simple.

Prigogine I., Entretien avec Ilya Prigogine, cite dans [Benkirane 02]

Nature has ceased to be simple and the complex systems constituting it - open systems made up ofnumerous entities in interaction - are dynamic systems which are perpetually changing. To understandthem, we need to model them.

Modeling is the action of intentionally, through the composition of symbols, designing and buildingmodels which could render a seemingly complex phenomenon intelligible. It also amplifies the reasoningof the actor who plans to deliberately intervene within the phenomenon, i.e., the reasoning which aimsto anticipate the consequences of possible action plans [Le Moigne 90].


Modeling complex systems raises numerous theoretical questions which must be answered. Whatmodeling approach should be adopted? How can the modeling intention be explained in the model?How can a complex system and its multi-level organization be characterized? How should the notion ofemerging overall behaviors based on individual behaviors be addressed? How can models with di!erentspace-time dynamics be integrated? How should the free will of the human taking part in these systemsbe taken into account? How can the singularity of a complex system be compared to the requirement ofreproducibility of the scientific method? How should models be validated? What are the links betweenreality and virtuality? and so on.

In virtual reality, the human is in the loop in multi-sensory interaction with multi-model and multi-disciplinary systems. This raises 3 main questions:

1. What is the place and the role of humans in these virtual environments?

2. What is the place and the role of the virtual entities moving in these environments?

3. What devices or systems should be deployed to ensure quasi-natural interactions between the humanwho is immersed in the environment and the entities populating it?

Answering the questions in this non exhaustive list will require defining a new methodological approachthat is suited to studying complex systems.

4.2 Methodological stakesThe more complex a science is, the more important it is, in fact, to establish a proper experimental critique, inorder to obtain facts which are comparable and free of sources of error. [...] The complete scientist is one whomasters both theory and practice. Although he must fully possess the art of establishing experimental facts, whichare the materials of science, he must also clearly account for the scientific principles which direct our reasoningin the highly varied environment of experimental study of natural phenomena. It would be impossible to separatethe two; head and hand. A skillful hand without the head to direct it is a blind instrument; the head without thehand to do things remains powerless.

Bernard C., Introduction a l’etude de la medecine experimentale, [Bernard 1865]

In order to establish a proper experimental critique, study and validation of complex system models re-quire their simulation. In the framework of virtual reality, simulation becomes participatory and proposesin virtuo experimentation of digital models while they are running. In virtuo experimentation should beconducted from multi-model modeling requiring the cohabitation or integration of the various models,while allowing a multi-level (local/overall) analysis of the system being experimented on. It should enablea cognitivism/constructivism dialogue between formal approaches and experimental approaches in orderto validate the models experimented.

Virtual reality, which enables users to experience sensory-motor activities in artificial worlds, in-struments Varela’s enactive approach where cognition is not only representation, but also embodiedaction: a system’s intelligibility is based as much on praxis in situation as on pure information processing[Varela et al. 93].

4.3 Technological stakesEvery computer program is a model, hatched in the mind, of a real or mental process. These processes, arisingfrom human experience and thought, are huge in number, intricate in detail, and at any time only partiallyunderstood. They are modeled to our permanent satisfaction rarely by our computer programs. Thus even thoughour programs are carefully handcrafted discrete collections of symbols, mosaics of interlocking functions, theycontinually evolve. We change them as our perception of the model becomes deeper, larger, more generalized,until the model ultimately attains a metastable place within still another model we are struggling with. Thesource of the exhilaration associated with computer programming is the continual unfolding within the mind andon the computer of mechanisms expressed as programs and the explosion of perception they generate. If artinterprets our dreams, the computer executes them in the guise of programs!

Abelson H., Sussman G.J., Sussman J., Structure and interpretation of computer programs, [Abelson et al. 85]

If art interprets our dreams, the computer executes them in the guise of programs and, in order toexperiment on them in virtuo, they need instrumentation. The virtuoscope, which gives VR digitalmodels their instruments, must of course have a large scale computing infrastructure available, a truevirtual lab bench for the 21st century. Creating this virtual lab bench must ensure real-time, reliable andalmost natural interoperability of systems to enable believable sensory-motor experiences on multi-modeland multi-disciplinary systems.


That way, we will have new tools for collective activity in virtual environments with less risk andlower costs at our disposal. The virtuoscope’s users will then be able to extract themselves from theirown spatial-temporal spaces to interact and participate within the same virtual universe in an artificiallife simulating the reality or the imagination of the modeler.

4.4 Societal stakesVirtual worlds must be realized, in other words, one must endeavor to update what is virtually present in them,to know the intelligible models that structure them and the ideas the develop them. The ”fundamental virtue”of virtual worlds is to have been designed with an end in mind. It is this end that must be realized, actuated,whether the application is industrial, spatial, medical, artistic, playful or philosophical. Images of virtuality musthelp us reveal the reality of virtuality, which is of an intelligible order and of proportional intelligibility to thegoal pursued, whether theoretical or practical, utilitarian or contemplative.

Queau Ph., Le virtuel, vertus et vertiges, [Queau 93b]

The “fundamental virtue” of virtual worlds is to have been designed with an end in mind. Indeed,the understanding of complex systems and their experimentation instrumented within the virtuoscopeshould make allow complexity to be mastered in all its societal dimensions.

• In technological terms (assisted design, virtual mock-up, simulation, etc.), in virtuo experimentingon virtual models or mock-ups takes place at each stage in the life of a new product - from disposablerazor to nuclear power plant -: from the idea of a product from its eventual disassembly to its design,prototyping and its maintenance.

• In cultural terms (virtual museum, virtual theater, interactive fiction, participatory arts, etc.), invirtuo experimentation places the spectator at the heart of the work of art, enabling him to becomeactor and creator (cre-actor).

• In educational terms (virtual training environments, preparing missions in hostile environments,etc.) in virtuo experimentation returns learning of know-how and interpersonal skills to its properplace.

• In health care terms (surgical operations, therapies, bio feedback, etc.) in virtuo experimentationlets patients play an active part, in full cooperation with the caregivers.

• In political terms (decision-making aid for urban planning, emergency services, environmental pro-tection, etc.), in virtuo experimentation provides deciders with the means to better assess thedi!erent scenarios being considered and test the di!erent possibilities by acting in the virtual en-vironment. In virtual reality, decision also becomes a simulation of action, as suggested by theneurophysiologist Alain Berthoz.

Therefore, decision is not just reason, it is also action. It is never a purely intellectual process, a logic gamethat we can put in an equation. Decision involves reflection, of course, but it already carries within it, whileincluding the elements of the past, the act which it will lead to.

Berthoz A., La decision, [Berthoz 03]

These few examples, selected amongst others, clearly illustrate the importance of the stakes related tothe understanding, study, experimentation, instrumentation and mastery of complex systems. In virtuoexperimentation in virtual reality thus appears to be one of the best ways to grasp this complexity.

There are two illusions which divert our minds from the issue of complex thought, and which must be dispelled.The first is the belief that complexity leads to the elimination of simplicity. Of course, wherever simplifyingthought falters complexity does indeed appear. However, it incorporates all that can bring order, clearness,distinction and precision to knowledge. Whereas simplifying thought splits up the complexity of reality, complexthought includes simplifying ways of thinking, insofar as possible. But it rejects the mutilating, simplistic, one-sizing, and in the final analysis, blinding consequences of simplification, simplification which takes itself for thereflection of what is real in reality. The second illusion involves confusing complexity with completeness. Indeed,the ambition of complex thought is to account for the linkages between subject fields which disjunctive thinking(itself one of the main aspects of simplifying thought) breaks down; this thinking isolates what it distinguishes,and obscures all that could link, interact or interfere. In this sense, complex thought aspires to multidimensionalknowledge. Yet, from the outset, it recognizes the impossibility of complete knowledge: for one of complexity’saxioms is that omniscience is impossible, even in theory.

Morin E., Introduction a la pensee complexe, [Morin 90]


References

[Abelson et al. 85] Abelson H., Sumangerssman G.J., Sussman J., Structure and interpretation of com-puter programs (1985), traduction francaise : InterEditions, 1989

[Bachelard 38] Bachelard G., La formation de l’esprit scientifique (1938), Vrin, Paris, 1972

[Bannon 91] Bannon L.J., From human factors to human actors : the role of psychology and human-computer interaction studies in system-design, dans [Greenbaum et al. 91]:25-44, 1991

[Barto et Sutton 81] Barto A.G., Sutton R.S., Landmark learning : an illustration of associative search,Biological Cybernetics 42:1-8, 1981

[Benford 95] Benford S., Bowers J., Fahlen L.E., Greehalgh C., Mariani J., Rodden T., Networkedvirtual reality and cooperative work, Presence 4(4):364-386, 1995

[Beer 90] Beer R.D., Intelligence as adaptive behavior : an experiment in computational neu-roethology, Academic Press, San Diego, 1990

[Benkirane 02] Benkirane R., La complexite, vertiges et promesses, Le Pommier, 2002

[Bernard 1865] Bernard C., Introduction a l’etude de la medecine experimentale (1865), Garnier-Flammarion, 1966

[Berthoz 03] Berthoz A., La decision, Odile Jacob, 2003

[Brooks 86] Brooks R.A., A robust layered control system for a mobile robot, IEEE Journal ofRobotics and Automation 2(1):14-23, 1986

[Brooks 91] Brooks R.A., Intelligence without representation, Artificial Intelligence 47:139-159, 1991

[Bryson 96] Bryson S., Virtual reality in scientific visualization, Communications of the ACM39(5):62-71, 1996

[Cadoz 94] Cadoz C., Les realites virtuelles, Collection DOMINOS, Flammarion, Paris, 1994

[Capin et al. 97] Capin T.K., Pandzic I.S., Noser H., Magnenat-Thalmann N., Thalmann D., Virtualhuman representation and communication in VLNET networked virtual environments,IEEE Computer Graphics and Applications 17(2):42-53, 1997

[Carlsson et Hagsand 93] Carlsson C., Hagsand O., DIVE – A platform for multi-user virtual environ-ments, Computers and Graphics 17(6):663-669, 1993

[Cli! et al. 93] Cli! D., Harvey I., Husbands P., Explorations in evolutionary robotics, Adaptive Behav-ior 2(1):73-110, 1993

[Collodi 1883] Collodi C., Les aventures de Pinocchio (1883), Editions Mille et une Nuits, Paris, 1997

[De Rosnay 75] De Rosnay J., Le macroscope : vers une vision globale, Editions du Seuil, Paris, 1975

[Drogoul 93] Drogoul A., De la simulation multi-agents a la resolution collective de problemes. Uneetude de l’emergence de structures d’organisation dans les systemes multi-agents, Thesede Doctorat, Universite Paris 6 (France), 1993

[Ellis 91] Ellis S.R., Nature and origin of virtual environments : a bibliographic essay, ComputingSystems in Engineering 2(4):321-347, 1991

[Ferber 95] Ferber J., Les systemes multi-agents : vers une intelligence collective, InterEditions,Paris, 1995

[Fikes et Nilsson 71] Fikes R.E., Nilsson N., STRIPS : a new approach to the application of theoremproving to problem solving, Artificial Intelligence 5(2):189-208, 1971


[Flach et Holden 98] Flach J.M., Holden J.G., The reality of experience : Gibson’s way, Presence 7(1):90-95, 1998

[Fuchs 96] Fuchs P., Les interfaces de la realite virtuelle, Les presses de l’Ecole des Mines, Paris,1996

[Fuchs et al. 03] Fuchs Ph., Arnaldi B., Tisseau J., La realite virtuelle et ses applications, dans Le traitede la realite virtuelle, 2eme edition, volume 1, chapitre 1, pages 3-52, Les Presses del’Ecole des Mines de Paris, 2003.

[George! et Lansky 87] George! M.P., Lansky A.L., Reactive reasonning and planning, ProceedingsAAAI’87: 677-682, 1987

[Gibson 84] Gibson W., Neuromancer, Ace Books, New York, 1984

[Gobel et Neugebauer 93] Gobel M., Neugebauer J., The virtual reality demonstration centre, Computerand Graphics 17(6):627-631, 1993

[Guillot et Meyer 00] Guillot A., Meyer J.A., From SAB94 to SAB2000 : What’s New, Animat?, Pro-ceedings From Animals to Animats’00 6:1-10, 2000

[Grand et Cli 98] Grand S., Cli D., Creatures : entertainment software agents with articial life, Au-tonomous Agents and Multi-Agent Systems 1(1):39-57, 1998

[Greenbaum et al. 91] Greenbaum J., Kyng M. (editeurs), Design at work : cooperative design of com-puter systems, Lawrence Erlbaum Associates, Hillsdale, 1991

[Hallyn 04] Hallyn F., Les structures rhetoriques de la science, Seuil, 2004

[Harrouet 00] Harrouet F., oRis : s’immerger par le langage pour le prototypage d’univers virtuels abase d’entites autonomes, These de Doctorat, Universite de Bretagne Occidentale, Brest(France), 2000

[Herrmann et Luding 98] Herrmann H.J., Luding S., Review article : Modeling granular media on thecomputer, Continuum Mechanics and Thermodynamics 10(4):189-231, 1998

[Hewitt 77] Hewitt C., Viewing control structures as patterns of message passing, Artificial Intelli-gence 8(3):323-364, 1977

[Holloway 92] Holloway R., Fuchs H., Robinett W., Virtual-worlds research at the University of NorthCarolina at Chapel Hill, Computer Graphics’92, 15:1-10, 1992

[Johnsen et Corliss 71] Johnsen E.G., Corliss W.R., Human factors applications in teleoperator designand operation, Wiley, New York, 1971

[Kallmann et Thalmann 99] Kallmann M., Thalmann D., A behavioral interface to simulate agent-objectinteractions in real time, Proceedings Computer Animation’99:138-146, 1999

[Kodjabachian et Meyer 98] Kodjabachian J.,Meyer J.A., Evolution and development of neural con-trollers for locomotion, gradient-following, and obstacle-avoidance in artificial insects,IEEE Transactions on Neural Networks 9:796-812, 1998

[Krueger 83] Krueger M.W., Artificial Reality, Addison-Wesley, New York, 1983

[Langton 86] Langton C.G., Studying artificial life with cellular automata, Physica D22:120-149, 1986

[Le Moigne 77] Le Moigne J-L., La theorie du systeme general : theorie de la modelisation, PressesUniversitaires de France, Paris, 1977

[Le Moigne 90] Le Moigne J-L., La modelisation des systemes complexes, Collection AFCET Systemes,Bordas, Paris, 1990


[Levy-leblond 96] Levy-Leblond J.M., Aux contraires. L’exercice de la pensee et la pratique de la science,NRF essais, Gallimard, 1996

[Luciani 00] Luciani A., From granular avalanches to fluid turbulences through oozing pastes : amesoscopic physically-based particle model, Proceedings Graphicon’00 10:282-289, 2000

[Mach 1905] Mach E., Erkenntniss und Irrtum, (1905), Wissenschaftliche Buchegesellschaft, 1976,cite dans [Hallyn 04]

[Mellet 04] Mellet d’Huart D., De l’intention a l’attention. Contributions a une demarche de con-ception d’environnements virtuels pour apprendre a partir d’un modele de l’(en)action.,These de Doctorat, Universite du Maine, Le Mans (France), 2004

[Meyer et Guillot 91] Meyer J.A., Guillot A., Simulation of adaptive behavior in animats : review andprospect, Proceedings From Animals to Animats’91 1:2-14, 1991

[Minsky 80] Minsky M., Telepresence, Omni 6:45-51, 1980

[Morin 77] Morin E., La methode, Tome 1 : la nature de la nature, Editions du Seuil, Paris, 1977

[Morin 90] Morin E., Introduction a la pensee complexe, ESF editeur, Paris, 1990

[Morineau 00] Morineau T., Context e!ect on problem solving during a first immersion in a virtualenvironment, Current Psychology of Cognition 19:533-555, 2000

[Noser et Thalmann 95] Noser H., Thalmann D., Synthetic vision and audition for digital actors, Pro-ceedings Eurographics’95:325-336, 1995.

[Platon -375] Platon, La republique, Livre VII (375 avant J.C.), Traduction de E. Chambry, Bib-liotheque Mediations, Editions Gonthier, Paris, 1969

[Queau 93a] Queau Ph., Televirtuality : the merging of telecommunications and virtual reality, Com-puters and Graphics 17(6):691-693, 1993

[Queau 93b] Queau Ph., Le virtuel, vertus et vertiges, Collection Milieux, Champ vallon, Seyssel,1993

[Renault 90] Renault O., Magnenat-Thalmann N., Thalmann D., A vision-based approach to be-havioural animation, Journal of Visualization and Computer Animation 1(1):18-21, 1990

[Schloerb 95] Schloerb D.W., A quantitative measure of telepresence, Presence 4(1):64-80, 1995

[Schuler et al. 93] Schuler D., Namioka A. (editors), Participatory Design : Principles and Practices,Lawrence Erlbaum Associates, Hillsdale, 1993

[Sheridan 87] Sheridan T.B., Teleoperation, telepresence, and telerobotics : research needs, ProceedingsHuman Factors in Automated and Robotic Space Systems’87:279-291, 1987

[Sims 94] Sims K., Evolving 3D morphology and behavior by competition, Artificial Life 4:28-39,1994

[Singh 94] Singh G., Serra L., Ping W., Hern N., BrickNet : a software toolkit for network-basedvirtual worlds, Presence 3(1):19-34, 1994

[Slater et al. 94] Slater M., Usoh M., Steed A., Depth of presence in virtual environments, Presence3(2):130-144, 1994

[Tachi et al. 89] Tachi S., Arai H., Maeda T., Robotic tele-existence, Proceedings NASA Space Teler-obotics’89 3:171-180, 1989

[Thalmann 96] Thalmann D., A new generation of synthetic actors : the interactive perceptive actors,Proceedings Pacific Graphics’96:200-219, 1996


[Tisseau et al. 98b] Tisseau J., Reignier P., Harrouet F., Exploitation de modeles et Realite Virtuelle,Actes GTAS’98:13-22, 1998

[Tisseau 01] Tisseau J., Realite virtuelle : autonomie in virtuo, Habilitation a Diriger des Recherches,Universite de Rennes I (France), 6 decembre, 2001

[Tisseau et Nedelec 03] Tisseau J., Nedelec A., Realite virtuelle : un contexte historique interdisci-plinaire, Revue internationale de CFAO et d’infographie 17(3-4):263-278, 2003.

[Varela et al. 93] Varela F., Thomson E., Rosch E., L’inscription corporelle de l’esprit, Seuil, 1993

[Vertut et Coi!et 85] Vertut J., Coi!et P., Les robots, Tome 3 : teleoperation, Hermes, Paris, 1985

[Vidal 84] Vidal F., L’instant creatif, Flammarion, Paris, 1984

[Wilson 85] Wilson S.W., Knowledge growth in an artificial animal, Proceedings Genetic Algorithmsand their Applications’85:16-23, 1985

[Witmer et Singer 98] Witmer B.G., Singer M.J., Measuring presence in virtual environments : a pres-ence questionnaire, Presence 7(3):225-240, 1998

[Wooldridge et al. 95] Wooldridge M., Jennings N.R., Intelligent agents : theory and practice, TheKnowledge Engineering Review 10(2):115-152, 1995

[Zahorik et Jenison 98] Zahorik P., Jenison R.L., Presence as being-in-the-world, Presence 7(1):78-89,1998

[Zwirn 03] Zwirn H., La complexite, science du XXIeme siecle ?, dans Pour La Science, numerospecial ”La complexite”, decembre 2003

In vivo, in vitro, in silico, in virtuo · In vivo, in vitro, in silico, in virtuo, 1st Workshop on SMA in Biology at meso or macroscopic scales, Paris, july 2, 2008. 3 Perception

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