Cédric Buche

Ronan Querrec

Pierre De Loor

Pierre Chevaillier
email: chevaillier]@enib.fr

MASCARET: A Pedagogical Multi-Agent System for Virtual Environment for Training

Keywords:

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Introduction

This study concerns the design of Virtual Environments for Training (VET). We want to immerse learners in their professional environment simulated using virtual reality techniques. This enables them to manipulate the environment so that they can "learn while doing". This idea is driven by the "constructivism" paradigm defined by [START_REF] Piaget | Behavior and evolution[END_REF] and can find a good implementation in virtual reality techniques as presented by [START_REF] Burdea | Virtual Reality Technology[END_REF]. Our definition of Virtual Reality is the one proposed by [START_REF] Tisseau | Virtual Reality : in virtuo autonomy[END_REF] who proposes to give autonomy to models evolving in the virtual environment by giving them the "triple mediation of senses, decision and action". So, the main developed idea is that virtual environments for training are heterogeneous and open multi-agent systems. Those Multi-Agent Systems (MAS) had been presented by [START_REF] Demazeau | From interactions to collective behaviour in agent-based systems[END_REF] using the VOWELS model considering a MAS with four vowels: Agent, Environment, Interaction and Organisation. It also has been use for collaborative work simulation by [START_REF] Clancey | Simulating activities: Relating motives, deliberation and attentive coordination[END_REF]. We consider the user of a virtual environment as other autonomous agents because he or she can interact with the environment and with other agents or users in the same way. Then, as Tisseau, we propose to add a last vowel, the letter U for User, in the VOWELS model.

Our goal is to provide an agent-based model to create Virtual Environment for Training (VET) integrating an Intelligent Tutoring System (ITS) in order to provide students with dedicated tutoring. ITSs are based on four models [START_REF] Woolf | Building knowledge based tutors[END_REF]: domain model [START_REF] Anderson | The expert module[END_REF], learner model (Self, 1998), pedagogical model [START_REF] Wenger | Artificial intelligence and tutoring systems[END_REF] and the interface model [START_REF] Miller | The role of human-computer interaction in intelligent tutoring systems[END_REF].

Comparing to STEVE [START_REF] Rickel | Animated agents for procedural training in virtual reality : Perception, cognition, and motor control[END_REF], which is a mono-agent system based on a virtual tutor, we propose a multi-agent system where each entity can contribute to the pedagogy. Moreover pedagogical skills are not imposed by our model; they are viewed as knowledge items manipulated by agents. These elements provide flexibility and adaptability to our VET. In [START_REF] Lourdeaux | An intelligent tutorial agent for training virtual environments[END_REF]) HAL is a pedagogical agent (based on a pedagogical model) helped by Environment-Agent and Scenario-Agent responsible to the detection of the learner actions and intentions. The present work goes further; it takes into account all the ITSs models and both the learner environment (physical and social context) and the pedagogical one are considered as a multi-agent system.

A major issue in multi-agent system is the definition of agents interactions. In our case, interactions have to be flexible and controlled by pedagogy. Therefore, we propose a model centred on the concept of organisation, which permits to structure these interactions. We show that information about organisation can also structure knowledge upon classical ITS models and thus allows agents to take pedagogical decisions.

After an overview on ITS models, we present our MASCARET model (Multi-Agent System for Collaborative Adaptive and Realistic Environment for Training). We start by defining a generic model, which is then derived to represent the different types of interaction in our VET. Next we explain how the different models of ITSs are taken into account by pedagogical agents, defining therefore the pedagogical organisation. Finally we briefly present the application of MASCARET to a fire-fighter training environment: SÉCURÉVI.

Intelligent Tutoring Systems

Intelligent Tutoring Systems (ITS) are computer processing systems for training incorporating communication techniques of knowledge and skills. They were conceived from the combination of Interactive Learning Environments (ILE) and Artificial Intelligence (AI) techniques. Such systems were developed with the objective to adapt speed and level of the knowledge representation to the student's needs. The system uses an internal representation of this knowledge and has possibility of reasoning.

In the ten last years, ITSs were used within the framework of training and had proved their effectiveness [START_REF] Shute | Rose garden promises of intelligent tutoring systems: Blossom or thorn? In Space operations and Research[END_REF]. For example, students using LISP tutor [START_REF] Anderson | Analysis of student performance with the lisp tutor[END_REF] finish their exercises 30% faster than those which receive a traditional training. The final examination shows a difference of 43% in result between the two methods.

Traditionally an ITS is described using four major functions or components [START_REF] Wenger | Artificial intelligence and tutoring systems[END_REF]. Thus, an ITS is composed of models, each one playing a particular role and contributing to ITS decision-making. The first ITSs were composed of an expertise on the domain, an expertise on what must be learned and a representation of what the student learned or misunderstood. [START_REF] Burns | Foundations of intelligent tutoring systems: An introduction[END_REF]) identify these components as the three models of an ITS. They correspond to the "expert module" [START_REF] Anderson | The expert module[END_REF], to the "diagnostic module of the student" [START_REF] Vanlehn | Student modeling[END_REF] and to the "instruction and curriculum module" [START_REF] Halff | Curriculum and instruction in automated tutors[END_REF]. Later, a fourth model has been introduced, augmenting the three first, the "interface module" providing knowledge about environment representation. Although there is no standard, an ITS generally consists of four models, identified in [START_REF] Woolf | Building knowledge based tutors[END_REF]:

• a domain model, representing the expert knowledge,

• a learner model, permitting to get the state of his knowledge at a given moment,

• a pedagogic model, permitting to carry out teaching choices according to the learner behaviour,

• an interface model, allowing the information exchange between system and user.

The domain model

The domain model represents the expertise on the domain [START_REF] Nkambou | Un modèle de représentation des connaissances relatives au contenu dans un système tutoriel intelligent[END_REF]. It is also called expert model since it defines the expert knowledge in a field of knowledge. The domain model does not contain only a description of competences to acquire, but also a representation of knowledge to transmit. It has to be able to propose several paths possibilities to achieve an objective. It consists of two components: the declarative knowledge and the procedural knowledge. On one hand the declarative brings a base of knowledge representing elements the professor would like to transmit. On the other hand the procedural proposes a reasoning system able to interpret the knowledge base.

The purpose of expertise on the domain is to allow a comparison with solutions suggested by the student.

For that, the domain model has to be able to generate solutions on the problems in the same context as the student one, so respective answers can be compared. Thus the system is able to determine differences and correspondences between student actions and those awaited. It can also evaluate performances and locate student difficulties. Lastly, the knowledge on the domain allows the generation of explanations related to the expert solution.

The knowledge representation requires the use of formalism. The logical formalism was one of the first formalisms suggested to represent knowledge. It uses a language, axioms and rules (logic proposal, fuzzy, modal, etc) allowing the representation of veracity, uncertainty, temporality, etc. Moreover cognitive sciences, which are interested in intelligence mechanisms, generally use a formalism based on graphs. It gathers, in the shape of graph concepts, notions representing knowledge and their interconnections. Semantic networks [START_REF] Quillian | Semantic memory[END_REF], networks with markers propagation [START_REF] Fahlman | Netl: A system for representing and using real-world knowledge[END_REF] or conceptual graphs [START_REF] Sowa | Conceptual structures: Information processing in mind and machine[END_REF] allow the representation of knowledge. An inference mechanism brings know-how. Lastly, a logical description formalism of knowledge representation called frame [START_REF] Minsky | A framework for representating knowledge[END_REF] is also used. A frame is a structure to represent a concept or a situation as "a room" or "to be in a room". This formalism associates for each knowledge a know-how.

2.2.

The student Model

Whereas the domain model contains expert knowledge on the problem resolution, the learner model [START_REF] Py | Quelques méthodes d'intelligence artificielle pour la modélisation de l'élève[END_REF][START_REF] Leman | Un modèle multi-agents de l'apprenant[END_REF] brings a measurement of student knowledge on this problem. It is as called diagnostic model since it allows measuring the student progression. Ideally, this model must contain an advanced representation of the student profile. It must provide all the specific aspects of each student behaviour and knowledge in the form of a model. The student profile is established and updated through the interactions he operates with his environment.

Using information of the learner model, the system adapts itself to the student. The profile must integrate learner knowledge on the domain model, called epistemic sub-model [START_REF] Delestre | Metadyne, un hypermédia adaptatif dynamique pour l'enseignement[END_REF]. The objective of the epistemic sub-model is to determine the state of student knowledge for concepts present in the domain. Moreover it must integrate his not-epistemic characteristics representing his pedagogical preferences or objectives, called the behavioural sub-model [START_REF] Delestre | Metadyne, un hypermédia adaptatif dynamique pour l'enseignement[END_REF]. It defines pedagogical objectives of the current exercise. The system must take into account these objectives, while being more or less flexible according to the training type. In the same way, the student must have the possibility to choose the point of view of such or such teacher. Lastly, the system must deal with the specific student capacities according to fields of teaching.

One of the learner model objectives is to allow the evaluation of each knowledge element to acquire. Incorrect behaviours can occur, also incorrect knowledge must be represented with the aim to identify errors. Several methods exist to evaluate knowledge to be acquired: 1. the Model Tracing compares stages achieved by the student and existing stages in procedural rules defined in the domain model. This approach was used by tutor LISP [START_REF] Anderson | The lisp tutor[END_REF]; 2. the Issue Tracing is a modification of Model Tracing. The purpose of this model is not to model the problem resolution process but rather to determine competences and solutions to use. An update of the competences acquired by the student is used. Tutor WEST [START_REF] Burton | An investigation of computer coaching for informal learning activities[END_REF] uses this method; 3. the Expert Systems analyze the student answers. The conclusions of domain model are used to update learner model. HANDLEBAR [START_REF] Clancey | [END_REF]) uses this method.

The representation of the epistemic and behavioural sub-models can use various methods: 1. the method called the overlay proposes to cut out the expertise in basic units. The student model is composed of a subset of these entities. The student knowledge is regarded as under part of the expert knowledge. An empty model corresponds to a student who would not have any domain knowledge, while a model identical to the expert corresponds to a student who would have reached the same level of control as a domain expert. Each knowledge item can be labelled with a discrete value (known/unknown) or a continue value (from 0 to 1). This principle was used by tutors WUSOR (Stansfield, Carr, & Goldstein1976) and HANDLEBAR. The errors made by the student are explained in terms of knowledge absence: it is the ignorance of such rule or such concept which leads the student not to do the best possibility. This model considers the student will learn nothing except what the expert had provided. Thus there is no mechanism to know not acquired knowledge and those which were not still presented; 2. the method called differential which is an extension of the overlay, where knowledge is separated considering if the student was exposed to such or such knowledge or was not. This method was used for tutor WEST; 3. the method called the buggy model which is also an extension of the overlay. The approach consists to present in the learner model rules whose application produces an incorrect result. This method was used by Brown and Burton in systems BUGGY [START_REF] Brown | A paradigmatic example of an artificially intelligent instructional system[END_REF] and DEBUGGY [START_REF] Burton | Diagnosing bugs in a simple procedural skill. Intelligent tutoring systems[END_REF]. Knowledge is represented by an elementary procedures network. Any correct procedure can be replaced by an incorrect procedure having the same domain applicability. From a set of answers given by a student, DEBUGGY builds the correct and incorrect procedures network which behaviour approaches most the student.

The pedagogical Model

The pedagogical model allows defining the pedagogical activities aiming at helping the teacher in the training process. It allows simulating the teacher decisional behaviour relative to a pedagogical intervention, based on differences between the domain model and the learner model. Then, the main objective of the pedagogical model is to answer to three questions [START_REF] Wenger | Artificial intelligence and tutoring systems[END_REF][START_REF] Lourdeaux | An intelligent tutorial agent for training virtual environments[END_REF]:

• When intervene? The pedagogical model determines when an intervention is desirable, if the student must be stopped or not. The trainers can intervene at various times: following errors made by formed, before errors in order to show up the risks of errors, following student questions, etc. To determine when intervene is a subtle decision. To guide a student, it is sometimes more effective to let the student seek during one moment than to stop him each time. On another side, been left to himself, the student probably will be lost. • Why intervene? Moreover, the pedagogical model determines why intervene. The objective can be to check student knowledge acquired or guide him in its training. Thus, [START_REF] Lourdeaux | An intelligent tutorial agent for training virtual environments[END_REF] proposes two types of interventions:

1. the pedagogical strategies related to the scenario modification allowing trainers to check student knowledge; 2. the pedagogical strategies related to the student guidance are distributed according to two categories: active methods (training by the action) and explanatory methods (training by the explanation). • How intervene? Much more, the pedagogical model determines the nature of the assistance. It can be a modification of the environment, the exercise or simply an addition of information. For that, it must take into account the student profile and the environment characteristics. Lourdeaux proposes a categorization in the various way of representing the "pedagogical assistances" according to various levels of realism: enrichment, degradation, simplification, etc [START_REF] Lourdeaux | An intelligent tutorial agent for training virtual environments[END_REF].

The pedagogical model must choose possible interventions allowing helping the student. For that, it can specify and control its interventions based on one or more "methods":

• the socratic method : the system asks questions to the student in order to encourage him to analyze its own errors (used by SCHOLAR and WHY); • the coaching method : the system leaves the student act and waits until he asks assistance (used by SOPHIE, WUMPUS and WEST);

• the learning by doing method : the system is active and encourages the student to select information and deduce orientations on the domain model; • the learning while doing method : the system remains in background task and only provides punctually tips.

The interface model

The interface model allows communication and finalizes the form by which the system wants to transmit information. This model is in co-operation with the diagnostic and didactic of the system. It transforms the internal representation of the system into comprehensible information for the learner. This model can transmit the same knowledge more or less clearly. Indeed, even if the pedagogical model decides course and contents of the didactic actions, the interface model deals with its final form. More generally, this model takes care of communications between the student and the system remainder [START_REF] Miller | The role of human-computer interaction in intelligent tutoring systems[END_REF]. Thus, it is in charge of the bidirectional communication between the internal representation of the system and a comprehensible interface for the student [START_REF] Wenger | Artificial intelligence and tutoring systems[END_REF]. We can define two directions for the communication:

1. System towards learner: it is the ergonomic of the interface, the way of defining which type of media to use to translate the system information. One of the difficulties in this context is the difficulty of adaptation to users. Indeed, various users will have different behaviours with various types of interface. For example, certain people will be more receptive to a purely visual interface, others with sound, etc. The interface designer will be able to discover which type of stimuli the user will be most receptive, based on the learner model; 2. Learner towards system: it is the detection of action / intention, the way of recovering information coming from the user in order to be able to analyze them. [START_REF] Burkhardt | Réalité virtuelle et ergonomie : quelques apports réciproques[END_REF] underlines the fact that the intention detection of the user is a central problem regarding interactions. It is necessary and important to separate action detection from of intention detection. Indeed, the intention does not imply the action and conversely.

We noticed three systems setting up one or more facets of the interface model:

• METADYNE [START_REF] Delestre | Metadyne, un hypermédia adaptatif dynamique pour l'enseignement[END_REF] is an adaptive hypermedia able to get student actions following hypertexts links used. Ergonomics is directed by the hypermedia, and consequently presents little interest in an immersive virtual environment, • HAL [START_REF] Lourdeaux | An intelligent tutorial agent for training virtual environments[END_REF] proposes to recognize actions using messages communication between environment agents and compares these data with a preset scenario to extract important information, • STEVE [START_REF] Rickel | Animated agents for procedural training in virtual reality : Perception, cognition, and motor control[END_REF]) also proposes to recognize actions using communication of messages between environment agents and build a scenario considering a final objective. It sets up an animated virtual tutor able to recognize and generate speech.

The use of the interface does not have to impose a burden for the training which would block the real training [START_REF] Wenger | Artificial intelligence and tutoring systems[END_REF]. The interface must use the advantage of existing communication conventions, like the natural language, while introducing some news, such as mouse [START_REF] Vigano | The virtual reality design tool: Case studies and interfacing open topics[END_REF].

The MASCARET model

Our goal is to train teams to collaborative and procedural work in a physical environment. In this case, we have to simulate in a realistic way this physical environment and the collaborative and adaptive team member's behaviour in the social environment. Evolution of those environments results from simulation of autonomous agent's local behaviour and their interactions. We propose a model, called MASCARET, where we use Multi-Agent Systems to simulate Collaborative, Adaptive and Realistic Environments for Training. This model aims at organizing the interactions between agents in the virtual environment and provides them abilities to evolve in this context. In addition, it allows the establishment of models necessary to the creation of Intelligent Tutoring System. In this context, the stress is put on the Organisation facet of the MAS. The organisational model specifies if agents may interact or not, the way they can do it and what they are expected to do according to their capabilities (their potential behaviour and the information they are supposed to hold). Because agents may have to adapt their behaviour according to this information, they must maintain knowledge about organisations. It is a major issue in a VET where learners and teachers have to build representations about their environment. They have to know how this environment is structured and it is necessary to control what kind of interaction may arise. The generic model of organisation is given in the next section. Its derivation to the modelling of specific interaction contexts is detailed in the followings.

The organisational model

The generic organisational model is presented as a UML class diagram (Figure 1). It is based on the concepts of agent, organisation, role and behavioural feature. (Hannoun, Boissier, Sichiman, & Sayettat, 1999) have already proposed an organisational model for multi-agent systems, but this model, dedicated to the collaborative realisation of procedures, is not enough generic to solve our problem. [START_REF] Ferber | A Meta-Model for the Analysis and Design of Organizations in Multi-Agent Systems[END_REF] have also proposed such a model called Agent/Group/Role, but this model seems to be more a pattern for MAS design than a model which formalizes the concepts of organisation and roles. In our model, the aim of the organisation is to structure interactions between agents; it enables each agent to know its partners and the role they are playing in the collaboration. The concept of role represents agent responsibilities in the organisation (corresponding to their behavioral features). Agents have then an organisational behaviour which permits them to play or abandon a role in an organisation. This behaviour enables also agents to take into account the existence of other members. This model is a generic model in the way that all the resulting classes are abstract. The organisational model is then derived to implement specific organisations. far he can delegate actions to his avatar: e.g. the actions corresponding to movement from points to points can be delegated to the avatar so that the learner can focus his attention on more specific learning objects.

Actions the learner is supposed to do and actions delegated to his avatar are specified by their respective roles.

Human organisation is not completely under the control of the VET: corresponding interactions are not mediated by the system. Anyway, assumptions may be made about these kinds of interactions and it can be decided to prevent them (when users are in different rooms) or to encourage them (by an invitation to ask a question to the teacher for example).

PedagogicalAgent

The physical interactions.

In a Virtual Environment for Training, the user's (learner and teacher) physical environment must be realistic, interactive and acts in "real-time". Then, to reach the constraints of virtual reality, models we use to simulate physical phenomena are obviously simplified. Therefore, the teacher may want, for pedagogical reasons, to inhibit some phenomena. For that, we must propose models which are compatible to a disconnection between the phenomena. Moreover, although all interactions have potentially effects on the two agents involved, we consider that the interactions between agents have a privileged direction. Then, the reactive agents' behaviour evolving in physical environment is to perceive situations and to act consequently. A practical limit of the individual based model is that each agent can potentially perceive all others. The complexity of the algorithm is in this case O(n 2 ). Then, we have to design rules to organize theses interactions between reactive agents. For that, we use the generic organisational model we have proposed before. In this case the organisation is a network where agents are connected together when they are in interaction. We call this organisation an interaction network (InteractionNet, Figure 3).

To represent the concept of privileged direction in interactions, we define two particular roles called source and target. The goal of source agents is to give information on their internal states to other agents (targets) so that they can compute the interaction's "strength" and their internal state. The interaction can be detected by the two agents involved, but, for "real-time" computation reasons, it is better if only one agent detects it (one of two agents or another one else). We then define a recruiting role which has the responsibility to maintain the knowledge of each agent upon the structure of the organisation. This means that an agent playing this role has to detect the interaction situations. The internal architecture of reactive agents matches the constraint of physical phenomena disconnection presented before, because an agent can have several reactive behaviour, each one participating in a different interaction network. This elementary behaviour (see ReactiveBehavior class in Figure 3) consists in the computation of a vector of internal state variables after the evaluation of inputs (from the interactions where the agent is a target) and presents a pertinent external representation of its state (output) to other agents (potentially targets of an interaction where the former agent is a source).

The social interactions.

The environment is also populated by more "intelligent" agents representing humans. They are undergoing the environment and acting on it as reactive agents, but the way they choose their actions is carried out on a higher level of abstraction. Those agents are various humans involving in the formation (learners, teachers) who are played by autonomous agents. In our case, the social environment is structured and each member knows its roles and those of its partners. In a specific organisation the interactions between the team members are also structured by the mean of domain specific procedures known by all members. We thus derive our generic organisational model to formalize this concept of team (Figure 4). We are interested in the case where the action's coordination between team members are already stated and written in procedure. On the other hand, the environment being dynamic, agents can need to adapt the scenario to the environment. The procedure must then have a semantic representation so that agents can reason above. Then we use a high level language to describe a procedure (Allen temporal logic).

The reasoning of team members relates on organisation, procedures and actions. We propose a model of agent having local organisational knowledge. A rational agent is divided into a decisional part and a part represented by modules of perception of the physical environment, communication and actions (Figure 5). It must carry out actions of the procedure and adapt to situations not envisaged. The procedure describes interactions between agents in an optimal situation, and leaves to the agent the responsibility to build implicit plans (not clarified in the procedure) considered as "natural" within an applicative situation. Moreover, the procedure coordinates actions of a semantic level which we call "actions trades" such as "sprinkling a fire" in the case of firemen procedures, whereas the implicit plans arrange actions of a generic semantic level for humans such as "going at a point". For that, the agent must be able to reason on actions and we propose a model of goal directed actions having pre-conditions and postconditions. Thus, before carrying out an action, the agent must make sure that pre-conditions of this one are checked. If it is not the case, it builds itself a plan by back chaining on pre-conditions and postconditions of actions. When an agent starts or stops actions, it broadcasts a message that enables other members to update their knowledge the evolution of the procedure. When this behaviour failed, the agent calls its organisational behaviour which can help it to find a solution with another team member. Thus, in a hierarchical organisation, when an agent has a problem which it cannot solve, it refers to its superior. Then, the superior has the responsibility to find a solution among its subordinates (if it does not find any, it refers to its own superior about it). We represent this mechanism by a method like a Contract Net Protocol.

The users interactions.

The avatar, in MASCARET, is not only the representation of the user but has also its own behaviour (reactive and rational). Therefore the avatar model in MASCARET is the same as rational agent. In order to provide the decision making responsibilities to the user (student) the link between the collaborative behaviour and the action module (Start Action message) can be inhibited. This inhibition is dynamic, means a learner can take the control of an autonomous agent during the simulation. It becomes then his avatar. At any time, the user can release his control on the avatar. Their adaptive behaviour is supported by the dynamics of roles attribution between the user and his avatar (see mediation organisation Figure 2).

All modules composing the avatar are still active and thus remain potentially usable. The Action's Result message provides information to the Facts database. The avatar has the knowledge on the action historic and on the current action running. In addition, the avatar is still informed about result of other member's actions and still informs them about action done by the user. The knowledge on the evolution of the collaborative procedure is still consistent.

As the collaborative behaviour of the avatar is inhibited, it does not call any more the organisational behaviour (in case of an action failure). It becomes the responsibilities of the user to find a solution.

Management of ITS models.

Our objective is to integrate a differentiated pedagogy based upon context. Intelligent Tutoring System (ITS) presented earlier, aims at providing students with dedicated tutoring. In MASCARET, the information presented by classical ITS models are accessible. The goal of this paragraph is to show how we can represent the four models of an ITS. In MASCARET, the models are seen as bases of knowledge constructed and manipulated by agents or humans (human or virtual).

The domain model.

The expert model represents the knowledge that the learner has to acquire. In our case this knowledge is essentially procedural, but also "knows-how" to act in the environment. Therefore the domain model presents knowledge on the social and physical environment. In order to represent this knowledge, this model is based upon the Team and InteractionNet models proposed by MASCARET. This mean that comparing to models presented in the ITS overview section, our formalism is based on a logical representation (action pre-conditions and goals), but also on a graph representation to explicit links between the actions of roles and interaction between the physical entities. The expert model is not represented as a special class but is represented by the set of organisations. But, in no way, the expert model must reference each instance of organisation. It only refers to the knowledge on organisations structures. An agent asking information to the agent maintaining the expert model, never asks information in a specific context ("which action the user can do now?"), which is the responsibility of agents maintaining the user model for example, but ask question in a generic case ("what actions can be done after this action in this type of organisation?") In that way the reification of the concept of organisation is very important. Then, about the social environment, the agent maintaining knowledge on the expert model is able to answer questions about roles, actions and procedures. This information is written by the expert according to models proposed by MASCARET. Therefore, as STEVE for example, the expert model is able to inform on pre-conditions or goal of actions, the order of those actions and responsibilities of each intervenient. The way this information is used depends on other agent behaviour. For example an agent can ask information on the goal of actions to explain it to the learner.

For the physical environment, the expert model maintains information on casual links between the different roles (sources, targets …) intervening in physical phenomena. This knowledge is written by the expert and the application designer. An agent can then ask information, for example what can change the direction of the lick of gas and have the answer that it is an agent playing role "source" " in a "gas propagation" interaction net.

The errors model.

As in some ITS, presented Py (1998), we consider errors are crucial information. Therefore we decided to introduce the errors model in our ITS. An error model is a knowledge base on classical errors done by learners. It can be compared to the buggy model presented earlier; its goal is to help to identify the error and determine the reason of this error. This information is written by the teacher and takes the form of the rule: "usually students do action C after action A; it is an error because something", where "something" is classically a domain-specific rule.

Using the organisation model of MASCARET, a pedagogical agent can detect errors on procedure ("not the right action or not done by the right team member"). The responsibility of the agent managing errors is then to exploit its knowledge about typical errors (the left part of the rule) in order to give more accurate information on the error (the right part of the rule). Therefore the tutor can perform a particular action, e.g. explain the "something". The agent managing errors can record information about error occurrences. This information may be used to enrich the knowledge about typical errors. It is under the control of the human teacher; in the future, the agent might learn it.

The learner model.

As seen earlier, the learner model could be divided on psychological information, curriculum, and learner current actions and state.

In MASCARET, a special agent maintains information on the learner current state and action. This agent is the avatar agent; it maintains knowledge on the actions done by the user, which roles he plays in which organisations. In that way it can by assimilate to an overlay method because it contains a subset of the expert model. Moreover, the avatar can plays roles in physical phenomena (InteractionNet), then it maintain also this knowledge and can inform on the influences the user is currently overcoming. We could have decided to give its pedagogical autonomy to this avatar. In this case, the avatar could decide to explain/do the next action or decide not to overcome some physical phenomena for pedagogical reasons. But we prefer to give the avatar its pedagogical responsibilities by the mean of pedagogical roles in pedagogical organisation and then let the teacher express its pedagogical rules which is the role of the pedagogical model. Psychological criteria of the learner are for example student's emotion state or level. In MASCARET, this information is not yet accessible, but we are planning to work on it, based on (Kermarrec,02) works.

The interface model.

This model permits the communication between the system and the user. It presents information to the student and detects his actions. Virtual environment for training has the specificity to tend to be immersive. Therefore the user is supposed to perceive and to act the more naturally as possible. In MASCARET, this model has not been represented yet. But we can consider that we have the same problematic as [START_REF] Lourdeaux | An intelligent tutorial agent for training virtual environments[END_REF] whose work is addressed to decision making and not technical gesture. This method is based on virtual behaviour primitives. This means the correspondence between values of sensors (FOB, mouse, gloves…) with actions (walk, take…) and the procedure knowledge.

We also want to detect the intention of the user to act differently according to the difference between the user actions and intention. We consider that the user verbalizes his intentions and then they can be detected by the system with voice recognition.

The pedagogical model.

The pedagogical model defined pedagogical strategies issued from psychological and didactic research.

We are particularly interested in collaborative strategies. Therefore the pedagogical model is based on knowledge about roles (pedagogical actions), interaction (exchange of information about the different ITS models) and organisation (action coordination to reach the shared pedagogical goal). To our point of view, the pedagogical model shares characteristics of domain model, where the domain is "pedagogy". As in former case, general knowledge about pedagogical organisation is needed, instead of particular configuration of a training session. The pedagogical organisation structure is explained in next section.

The pedagogical interactions.

Pedagogical agents are defined as agents playing pedagogical roles. In MASCARET, potentially any agent can hold pedagogical skills. So these agents may have the following representations:

1. an autonomous artificial agent, which can have or not a representation in the virtual environment;

2. an avatar of a teacher playing no roles in the social environment of the trainees;

3. an avatar of a user (teacher or trainee) playing an active role in the environment (in this case the agent has three types of role: domain specific, proxy of the user and pedagogical ones);

4. an autonomous artificial agent simulating a human or a physical object (it is part of the environment and also exhibits pedagogical skills).

The contribution of these agents to the different ITS models presented is this article is obviously not the same. The first category of agents corresponds typically to tutors of traditional ITSs: they maintain information about the ITS models and communicate it to other pedagogical agents. The teacher can be personified in the environment in order to facilitate the communication with learners. The contribution of the learner avatar is important because it can gather information about the learner (user-avatar interactions). As the avatar participates also to social organisation it holds information about the domain (procedures to be performed). A pedagogical agent participating to the realisation of a collaborative procedure can play pedagogical roles as a companion or a troublemaker; in this way its behaviour is part of the pedagogical model. Physical objects of the environment are knowledge items of the domain model; they constitute also elements of the interface model (to control the way information is presented to the learner and the type of behaviour they can trigger in reaction to a user action in a particular pedagogical context); finally, to a certain extend they can also contribute to the pedagogical strategy: they can perform actions like to inform the user about its structure or its potential behaviour. Generally, these agents are mostly reactive than proactive agents and the pedagogical strategy is under the responsibility of deliberative agents like artificial tutors.

Every pedagogical agents share the same goal: to increase the student's skills. Different strategies may be applied to reach this goal. The pedagogical organisation defines the different roles that have to be played in a particular pedagogical context, i.e. the sets of pedagogical action to be performed. Figure 6 gives examples of pedagogical actions. Notice that each action can contribute to a different objective.

The pedagogical action Suggest informs the user about the next action. It provides goal and preconditions on the next action. The pedagogical action Show simulates such action. The pedagogical action Explain provides current action goal.

Strategies are defined by roles, corresponding to sub-goals. For example, the goal of a disturbing strategy is to suggest solutions that can be erroneous [START_REF] Chan | Learning compagnion systems[END_REF]. It is a mean to enforce the learner to evaluate his self-confidence in his own solutions. For example this strategy may consist to modify the orientation of the wind in order to show up gas propagation. Disturbing the learner is achieved by modifying the behaviour of the agents playing the Source role in this InteractionNet. Using the four models, a pedagogical multi-agent system can help student with dedicated tutoring. We proposed an overall process resulting in interactions (data-flows) between the five ITS models (Figure 7). The pedagogical process, which constitutes the resulting behaviour of the MAS, is a five steps cycle. First, a pedagogical agent observes the student's action using the interface model. The avatar of the student has the knowledge on the current running action. Such observation permits to update the learner model. Second, we compare the expert model to the learner model. That way, we are able to detect an error. For example, if the goal of an exercise is to respect a procedure. The student achieves action A and starts action C. The expert model provides information on the procedure. Therefore we know that after action A the student should start action B. The comparison detects an error corresponding to a layout. The error detection uses the error model and updates the learner model. In addition, we can increase the errors model. According to information on the error and using the pedagogical model (strategies), the learner model (level, emotion state ...) and the Context (Figure 7) the pedagogical agent selects a pedagogical action. Finally, the pedagogical action selected is represented to the student using the interface model.

SÉCURÉVI SÉCURÉVI (Security and Virtual Reality

) is an application of MASCARET to civil safety (Figure 8). It is dedicated to the training of fire-fighters officers for operational management and for commandment. A complete description of SÉCURÉVI is presented in [START_REF] Querrec | SécuRéVi : virtual environments for fire-fighting training[END_REF]. In a typical exercise, gas lick in an industrial site, the physical environment is constituted of the site where exercises take place as well as physical phenomena (fire, smoked, water jets ...) being able to intervene.

The application designer's work in SÉCURÉVI is essentially to implement elements of physical and social environment by inheriting MASCARET. Thus, the application designer has to conceive his own agents to simulate a specific phenomenon. This is possible by inheriting INAgent as well as its reactive behaviour (inheriting Reactive Behavior). Then he defines the interactions networks (InteractionNet).

The application designer of social environment composed of FPT teams (Fire engine Thunders Pumps) in charge of the incident attack or CMIC team (the Intervention Chemical Movable Cell) follows the same path by inheriting model of social environment proposed by MASCARET. Thus, the application designer has to describe new teams and roles as well as actions that agents have to perform. SÉCURÉVI is implemented using the platform AREVI/ORIS [START_REF] Harrouet | oRis : un environnement de simulation interactive multi-agents[END_REF].

The domain model consists first of knowledge on FPT and CMIC team structure. Each type of organisation is structured by four or five roles and up to twenty procedures. The learners play roles of leaders intervening in those teams during the incident. Teachers can participate in the simulation to trigger malfunctions, help the learners or play a role in a team. Then we can create two mediation organisations, one for the learner and the other for the teacher. In each one, the user avatar intervenes. In the learner mediation organisation, the role of the learner is to choose the action he has to do according to the action done by the other team members. The role of the avatar is to realize in the environment the action ordered by the learner and to overcome some of the physical phenomena in the environment. In that way the avatar knows student historic and current action running. Therefore the avatar plays a role in a pedagogical organisation where he has the responsibility to maintain the student model. As the avatar is also in charge of detecting learner's action, it has the responsibility who manages the interface model. In fact, this agent has information about roles, and actions played by the learner. It can, using communicating with the agent maintaining the domain model, have information about all actions done by the learner. Then, according to pedagogical rules, it can choose to propose an interface where the learner has to verbalize the next action or select in a list constructed from the information retrieved by the avatar. But this agent is not the one to manage this interface model; another one can propose information on the environment. For example, the agent could display elements that are not visible in the real world as wind curve or gas cloud, in order to specify to the student specific conditions.

The teacher can also intervene in the simulation, by the mean of his avatar for example to play a role in the same team of the user. In addition, he can participate to a pedagogical organisation where he has the responsibility to decide which pedagogical action to do, and another agent, not visible in the environment realizes those pedagogical actions. For example the teacher may want to disturb the learner by modifying the environment. Then, if the mission goal is to stop gas propagation, disturbing consists to show up gas propagation. That means an agent will ask to the agent maintaining the domain model the roles that can modify this phenomenon. Then it will ask to the agents playing these roles to change their behaviour to increase the wind power or show the wind direction.

Conclusion and future works

Considering a virtual environment for training as a multi-agent system, we propose the MASCARET model. It provides a framework to design multi-agent systems dedicated to collaborative, adaptive and realistic environment for training. This VET aims to put trainees in operational position by simulating their physical and social environment in such a way that they can learn to perform some collective tasks. These tasks are described as sets of actions, which define roles, allocated to agents representing human actors. We stated that the system is adaptive in the way that the set of instantiated agents, roles allocation and agent decisional rules are not imposed upon pedagogical designer: all these elements can be dynamically defined in order to adapt pedagogy to learner profiles and pedagogical objectives. Not only has the behaviour of pedagogical agent to be adaptive but the physical environment ones too: it is necessary to remain in control of reactive objects manipulated by learners, or having influence on them, and more precisely of the type of physical phenomena to be activated or inhibited. Physical environment behaviour has to be controlled but must remain consistent in order to be intelligible to learners.

We stressed the point that information about potential interactions and action realisation are a major issue in the management of pedagogical knowledge, not only for teachers but also for learners. This knowledge is based on piece of information about organisations that structure interactions. We have made no assumption about the nature of these interactions and the way agents perform their actions because we think that this issue is still open. We agree that it will be helpful to propose solutions concerning this point to VET designers. It is the major objective of our future works.

We have shown how to take into account the four classical ITS models (enriched by the error model) in an agent-based VET. A key point is that knowledge about these models is distributed among agents that perceive actively their environment and exchange information, according to the pedagogical role they play. The resulting behaviour of the multi-agent system defines the pedagogical process we propose.

Our objective is that MASCARET will be multi-strategic VET as in [START_REF] Mengelle | A multi-agent architecture for an ITS with multiple strategies[END_REF]. It will be achieved by the specification of different pedagogical roles. First, we are interested by the tutor role.

Using the notion of procedure as describe in the social environment, the agents playing this role can provide a deductive reasoning and explications. Second, we propose the use of the role of companion [START_REF] Chan | Learning compagnion systems[END_REF]. The companion is a virtual actor that will cooperate for the realization of tasks, exchange ideas on the problem and share learner's goals. We are also interested in the role of troublemaker [START_REF] Aïmeur | Cooperative learning strategies for intelligent tutoring systems[END_REF], which goal is to disturb the learner by proposing solutions that can sometimes be erroneous. Finally, we want to provide to our system the possibility to adapt pedagogical behaviour to a specific student. In this optic, the choice of pedagogical actions will be more adaptive. Due to the number of input variable and pedagogical rules, we envisage the use of machine learning techniques as classifiers systems [START_REF] Wilson | ZCS: A zeroth level classifier system[END_REF].

Figure 1 .

 1 Figure 1. The generic organisational model (UML class diagram).

Figure 2

 2 Figure2gives examples of different instances of specific organisations which can be modelled using MASCARET. This figure is not an exhaustive view of what organisation may be and many other organisational entities may be imagined. Interaction patterns may differ by the number of agent involved and the roles they play. Notice that one agent can play roles in different organisations. Physical organisation specifies the interactions that occur between entities compounding the physical environment of the trainees. Because the participation of an agent to an organisation is explicit, the designer of the training session can decide to activate or ignore different kinds of physical interactions. Thereby the difficulty level of the exercise can be controlled. Virtual human and user's avatar may be involved (or not) in physical organisation because they have to manipulate it and to undergo their environment (e.g. depending on its learning experiences, a fire-fighter trainee may or not have to undergo the toxicity of a gas due to an accidental lick). Social organisation represents social interactions that are a major point in collaborative training. This kind of organisation involved agents representing humans: virtual humans (their expected behaviour is simulated), avatars that represent users' actions (trainees, teachers or co-workers). Pedagogical organisation allows to specify what trainees are supposed to do and what kind of actions and knowledge may be performed by pedagogical agents. These agents can be artificial agents (as an intelligent tutor in an ITS) or humans. In this last case, their actions are mediated by their proxy (see mediation organisation bellow). Pedagogical agent can play a wide variety of roles, e.g. tutor, domain experts, learning companion, troublemaker … Mediation organisation makes explicit the way the user can interact in the virtual environment and how far he can delegate actions to his avatar: e.g. the actions corresponding to movement from points to points can be delegated to the avatar so that the learner can focus his attention on more specific learning objects. Actions the learner is supposed to do and actions delegated to his avatar are specified by their respective roles. Human organisation is not completely under the control of the VET: corresponding interactions are not mediated by the system. Anyway, assumptions may be made about these kinds of interactions and it can be decided to prevent them (when users are in different rooms) or to encourage them (by an invitation to ask a question to the teacher for example).

Figure 2 .

 2 Figure 2. Different types of organisation in MASCARET.

Figure 3 .

 3 Figure 3. The Interactions Network.

Figure 4 .

 4 Figure 4. The Team model.

Figure 5 .

 5 Figure 5. Architecture of rational agents.

Figure 6 .

 6 Figure 6. The pedagogical agent / role / actions.

Figure 7 .

 7 Figure 7. The pedagogical process.

Figure 8 .

 8 Figure 8. Picture from SÉCURÉVI.

Biographies

Cédric Buche received French Graduate Engineering Degree in July 2002. He is a PhD student in Computer Science at the CERV (Virtual Reality European Centre held at Brest, France). He integrated the Immersive and Participative Simulation project in September 2002. The objective of the project is to simulate complex systems integrating humans; these humans being spectators, actors and creators. His PhD subject concerns to design of an ITS integrating adaptive pedagogical behaviour in order to provide dedicated learning to student in a virtual environment for training. His research topics are multi-agent systems, artificial intelligence, fuzzy cognitive maps, soft computing and machine learning techniques.

Ronan


Pierre De Loor
email: deloor@enib.fr

Romain Bénard
email: benard@enib.fr

Cyril Bossard
email: bossard@enib.fr

Interactive co-construction to study dynamical collaborative

Keywords: Co-construction, Context, Case Base Reasoning, Dynamical Situations, Team Sport

Introduction

Our research work aims to study collaborative and dynamic situations through virtual environments [START_REF] Bénard | Context's modeling for participative simulation[END_REF][START_REF] Bénard | Understanding dynamic situations through context explanation[END_REF]. These kinds of situations are especially interesting related to specific psychological features [START_REF] Dillenbourg | What do you mean by collaborative learning[END_REF]. Typically, we can meet them in the team sport area, which is our experimental aid [START_REF] Argilaga | Detection of real-time patterns in sports: Interactions in soccer[END_REF]. Inside the virtual environment, users may be able to interact with autonomous agents and/or human team members [START_REF] Salas | Handbook of virtual environments: Design, implementation and applications, chapter Team training in virtual environments: An eventbased approach[END_REF]. Various combinations of multiple trainees, human or virtual, may be brought together for a training exercise. In this perspective, and in collaboration with experts in team sport, we develop the simulation tool CoPeFoot (for Collective Perception in Football). This interactive simulator lies on the tool for virtual reality AREVI [16] 1 .

1 AREVI is build-up by the LISyC (http://lisyc.univ-brest.fr) and is available on http://sourceforge.net/projects/arevi/ CoPeFoot is meant for training coaches or referees. Football players training requires more specific skills, such as technical gestures, which are not within the scope of our work. However, our work focuses more on collective decision-making than on technical skills. In this view, two problems must be investigated:

1-Dynamic and collaborative situations cannot be described by sequences or synchronization unlike to procedural activities [START_REF] Buche | MASCARET: Pedagogical multi-agents system for virtual environment for training[END_REF]. Decision making in these complex environments is characterized by severe time stress, high stakes, uncertainty, vague goals, and many organizational constraints [START_REF] Cannon-Bowers | Establishing the boundaries of a paradigm for decision-making research[END_REF]. Consequently, the outcome of players' actions are practically unpredictable. Moreover, a lot of psychological considerations must be taken

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into account (weariness, errors), as well as physical considerations (wind, state of the grass, ...). Scenarios or sequences are reduced to episodic local modifications, without temporal persistance.

2-Interactions between protagonists (virtual or

human teammates and opponents) have to be conducted in a manner resembling real-life contexts [START_REF] Oser | Training team problem solving skills: an event-based approach[END_REF]. The behavior of virtual players has to be believable. This credibility is relative to the domain and to the aim of the simulation. We distinguish credibility from efficiency: In order to be relevant, the simulation must exhibit behavior which can be well-identified by users. The virtual simulation must cross refer to the real experience. The definition of these behaviors lies on psychological considerations rather than on mathematical optimization. Consequently, they are not easily formalizable.

The simulation setting of dynamic and collaborative situations cannot be obtained by the use of classical virtual story telling tools or languages [START_REF]Virtual Storytelling. Using Virtual Reality Technologies for Storytelling[END_REF]. Likewise, systematic approaches, such as reinforcement learning [START_REF] Sutton | Reinforcement Learning[END_REF] or classifier systems, [START_REF] Holland | Adaptation in Natural and Artificial Systems[END_REF] are conflicting with the credibility assumption. Even if these practical methods are used to define behaviors in collaborative and dynamic situations [START_REF] Sanza | Adaptive behavior for cooperation: a virtual reality application[END_REF][START_REF] Karol | Case based game play in the robocup fourlegged league part i the theoretical model[END_REF][START_REF] Ros | Retrieving and reusing game plays for robot soccer[END_REF], they are based on optimization criterion. Consequently, the generated behavior is not natural and cannot be used for human training or study. To follow this way, one must define criterion as difficult to obtain as an explicit description of the behavior itself! For these reasons, we argue that the coconstruction of knowledge and know-how, by the way of interactions between experts and machine is a relevant proposition. To do that, virtual reality and augmented virtuality (addition of extra representations into the virtual universe) allow rich interactions between users and models. It is necessary to define relevant interactions and associated behavioral models. Our contribution lies on the use of case base reasoning [START_REF] Aamodt | Casebased reasoning: Foundational issues, methodological variations, and system approaches[END_REF] associated with context modelisation [START_REF] Gonzalez | Contextbased representation of intelligent behavior in training simulations[END_REF] for the co-construction of dynamic and collaborative situations. The plan of this paper is as follows. The first section 2.1 clarifies the concept of interactive co-construction. Next, we present briefly the functioning principles of CoPeFoot, especially the context base reasoning (section 2.2).

Figure 1: Interactive co-construction

Section 2.3 introduces the setting up of coconstruction with CoPeFoot. In the final section 3, we focus on results related to a first credibility evaluation felt by some users.

Interactive co-construction 2.1 Principle

Interactive co-construction concept is based on both constructivist assumptions [START_REF] Piaget | L'épistémologie génétique[END_REF] and enactionism [START_REF] Varela | The Embodied Mind[END_REF], even on interactionism [START_REF] Bickhard | Interaction and representation[END_REF] point of view about cognition. Interactions not only allow the user to create his own-representation of a problem but allow the user to modify the problem itself. Conversely, such modifications will affect future interactions and so on. There is a construction of the representation of the problem and of the problem itself. It's a coconstruction. Moreover, these interactions occur in a social environment because protagonists share the same workplace, the common problem and the same goal. In our instance, the problem is to understand and/or to build collaborative and dynamical situations. Augmented virtuality is used to allow the modification of simulated situations. To do that, graphical interactive objects stand for virtual users behavioral models refinements. Consequently, we use

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a double loop of interactive simulation, described by figure 1. To carry out this double loop, we use context base reasoning within a virtual environment. We will now explain the principles of context base reasoning.

Context base reasoning within CoPeFoot

CoPeFoot rests on the multi-agent classical assumption that interaction between individual models allows to reach relevant collective behavior [START_REF] Ferber | Multi-Agent System: An Introduction to Distributed Artificial Intelligence[END_REF]. Our constraints of credibility led us to consider context as a modeling key element. Moreover, the coconstruction purpose of our work led us to use case base reasoning (CBR) principles to carry out action selection of virtual players. Indeed, this kind of reasoning comes from analogy reasoning in the area of psychology [START_REF] Aamodt | Casebased reasoning: Foundational issues, methodological variations, and system approaches[END_REF]. Each encountered situation can be associated with another similar well-known and wellresolved situation. The question is to define how to associate situations to choose the more relevant and how to adapt one situation to another. With a relevant and tractable adaptation, we hope to obtain improvable human like behavior for co-construction. In the CBR area, some studies focus on the use of the user experience with the help of interaction traces [START_REF] Champin | MUSETTE : a framework for Knowledge from Experience[END_REF]. For example, within the MUSETTE model [START_REF] Champin | MUSETTE: Modeling USEs and Tasks for Tracing Experience. In Workshop 5 'From Structured Cases to Unstructured Problem Solving Episodes For Experience-Based Assistance[END_REF], different steps allow to collect traces with the help of an observer agent. Such traces are analyzed and transformed in order to be presented to the user who can interact with them.

Our approach follows the same way considering that traces are cases. Each case is based on the representation of interactive context (which also includes actions and intentions). In the area of knowledge management, complementary use of CBR and context is frequently used [START_REF] Brézillon | Context in problem solving: a survey[END_REF]. In our case, a context is a tuple of perceptions. Each perception is defined by semantics considerations on the domain (here, football). Examples of such perception are the fact that a player is marked (followed by an opponent) or the fact that a team mate call for the ball. Technical description of our selection mechanism is out of the scope of this article. For mark, we can find some common features with the dynamical memory of CHANK [START_REF] Roger | Dynamic Memory Revisited[END_REF]. Interested readers can carry forward [START_REF] Bénard | Understanding dynamic situations through context explanation[END_REF][START_REF] Bénard | Raisonnement en contexte pour la simulation participative et l'étude des situations dynamiques collaboratives[END_REF]. Here is an overview of this mechanism: Each perception is a predicate which use variables. Each variable can be unified with values coming from the simulation. Thereby, perceptions are generics and are brought together in a tree which organizes the case base. This organization enables us to bring similar cases together, and thus to obtain an anytime research algorithm. Figure 2 summarizes the initial simulation loop of CoPeFoot : Context is a filter and a data analyzer of physical perception coming from AREVI (objects, sounds, time). Outputs of this filter are perceptions like qualitative distances between players, information on marking, stress, and so on ... This is the CBR elaboration step. This context is compared with cases of the base and allows the selection of one case (recall step). Finally, with the help of semantics information associated to the context, the case is adapted to the current situation and the virtual player can act in the virtual universe (adaptation). The co-construction principle (figure 1) is implemented within CoPeFoot in the following manner: During the interactive simulation of dynamic and collaborative situations, one or some users guide one or some avatars. Generally, they try to succeed in a specific exercise. It is the stage 1 (imitation) of the figure 3: For each action performed by the user, the system tries to learn a case representative of the current state of the simulation. By this way, it improves the base and then learns by imitation.

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Corresponding UML 2 activity chart is described on figure 4. Remember that a case includes not only context but also the intention of the player and a set of potential actions. The intention can be estimated with the help of semantics information associated with actions. During this stage, each time a new case is inserted, one timed maker is memorized. This marker will be used during the second stage (see next subsection).

Figure 3: The two stages of the co-construction

This machine learning algorithm works a priori because it is not based on a human explicitation by the user itself. The algorithm can not do more than statistical estimation on the frequency of user's choices to estimate intention. In other words, the context is not well-organized or hierarchized to take into account the psychological influence of each perception for each decision. In fact, the recalling step of the case base reasoning cycle is using weight associated with each perception and relative to each case. During the learning stage, no weight is computed. Consequently, inserted cases are not really 2 Unified Modeling Langage (http://www.uml.org/) relevant from a human decision point of view and then, they are not appropriate for a good generalization in the base. Indeed they include all possible perceptions of the player, more or less relevant. This creates an information noise. If such cases are reused for the control of a virtual player in another exercise, they risk to be inadequately closed together by the recall step of the CBR. Simulated behaviors will become unbelievable to improve the case base with the help of augmented virtuality. The second stage (stage 2 : revision) uses the case base as memorized traces. With the help of this traces, it is possible to replay the exercise. The avatar of the user becomes an autonomous virtual player. Such replay can be used to review the same sequence, like a 3D video recorder, but from another point of view. Such a tool is really relevant for training [START_REF] Herviou | La perception visuelle des entits autonomes en ralit virtuelle : Application la simulation de trafic routier[END_REF]. Moreover, during this stage, some new graphical objects are included in the virtual universe. This augmented virtuality aims to express features of the context, relative to the domain, which can be the cause of a decision. The user can interactively modify these features in order to explicit the decision-making from a human perspective. It is the second loop of the co-construction principle (see figure 1).

To do that, each time a timed marker is encountered by the simulator, the simulation is stopped. The user must select contextual features which seem important

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to him to make the actual decision. He must also affect a weight to each remaining features. This weight will be used in the future by the recall step of the CBR. A mechanism allows the revision of the case base (deleting unused perception for the case, adding weight and re-ordering the case base tree) to improve the human like aspect of the decisionmaking of the virtual user. The user deletes some of this contextual perceptions or affects a weight to the others. He can also correct the intention and the action associated with the case. When he does that, he takes part to the construction of the situations. Furthermore, he observes the consequence of his modification. This observation helps him to understand the collaborative situation. Whatever the user is -player, referee or handler -he makes some back and forth between stages 1 and 2. He can play with other protagonists with the distributed version of CoPeFoot which do the some things. By this way, the system allows a social construction of the representation of the behavior of players within dynamical and collaborative situations.

Evaluation

We have insisted on the behavioral credibility needed for our purpose of studying collaborative and dynamical situations. We have claimed two assertions:

1. The co-construction allows the improvement of the credibility of the interactive simulation. The two reasons are that : 1) we take into account a human point of view of the decision-making instead of using rational and optimized behaviors and 2) by some back and forth between stage 1 and stage 2 of CoPeFoot, the behavior and the situation are progressively modeled.

2. This approach allows a better appropriation of dynamic and collaborative situations by users. This appropriation leads to the learning and the understanding of such situations. These two assertions must be evaluated. In a first time, we focus on the first one. It is essential to carry out the second one. To do that, we have made an experimentation inspired by the TURING Test. The purpose is to evaluate the part of error for the recognition of autonomous virtual players rather than avatar (representation of a player controlled by a real VRIC LAVAL VIRTUAL PROCEEDINGS 2008 -90 human). CoPeFoot is distributed on the network and the name of user can vary from 0 to 22.

The evaluation protocol is this one : We use a case base defined by the way of stage 1 and 2 by expert and novice. We don't evaluate these stages. Twenty persons are divided in five clusters. Two servers allow two simulations in the same time in order to avoid communication beetwen persons in the same simulation. In the same way, the persons are distributed into three different rooms. They don't know how many players are avatar or autonomous agent. This number is given randomly by the program for each simulation. The better credibility of the autonomous players matches to 50% errors per user. Indeed, in this case, this user does not differentiate an avatar from an autonomous player. Figure 7 illustrates the distribution of users according to their error rates.

Even if the number of persons is limited, they do about five exercises each and the results are promising. They tend to show that the behavior of our virtual players creates trouble in the minds of most participants. Then, this experimentation is conclusive. Nevertheless, it is only an evaluation of the behavioral credibility (the first of our assertions). We now have to evaluate the relevance of our approach for the learning and the understanding of collaborative and dynamical situations.

Conclusion

This article has sought to provide a framework of coconstruction to study dynamic and collaborative situation. In addition to providing a theoretical modeling, it remains to do further development and an effective construction of knowledge through simulation within different protagonists. As suggested in introduction, expert players are not first concerned by such an approach. However, decision making skills can be trained by players, referees or coaches. The fact that the user can interact with virtual agents' perception parameters allows to test hypothesis. In this view, the user is constructing a representation through the two loops presented in section 2.1.

Like most propositions, this one presents some limits. For example, we bet that expert decision maker use definite perception during CBR elaboration step. A really psychological expertise is needed to minimize this bias. A second limit concerns collective behavioral credibility obtained through agents interactions. Our first investigation focuses on individual credibility and is not sufficient. To evaluate collective behavior, we actually hold investigations with expert football players. This research aims to observe emergent representation thanks to actions of multiplayers in our virtual environment as [START_REF] Trajkovski | Autonomy through self-organization: Interactivism and emergence in a virtual environment[END_REF] propose.

Figure 2 :

 2 Figure 2: Context base reasoning within CoPeFoot 2.3 The co-construction double loop within CoPeFoot.

Figure 4 :

 4 Figure 4: UML Activity chart of the imitation learning stage

Figures 5

 5 Figures 5 and 6 illustrate this stage : During the replay, the user can walk-around the scene and visualize contextual features built during the first stage. Examples of features are the following: A crossed ball close by a player means that he is calling for the ball. A compass means the perception of orientation, an arrow means the perception of a distance, a red circle means that a player is marked, and so on.

Figure 5 :

 5 Figure 5: Example of stage 2 : The restituation by the 3D video recorder. All possible perceptions are displayed without any hierarchy.

Figure 6 :

 6 Figure 6: Example of stage 2 (suite) : Elimination of some contextual features and weighting of others.

Figure 7 :

 7 Figure 7: Players distribution according to their errors.