An Intelligent Collaborative Virtual Environment forTeam Training — A Preliminary Report

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An Intelligent Collaborative Virtual Environment for

Team Training A Preliminary Report

Ral A. Aguilar

Universidad Autnoma de

Yucatn

[email protected]

Anglica de Antonio

Universidad Politcnica de

Madrid

[email protected]

Ricardo Imbert

Universidad Politcnica de

Madrid

[email protected]

Abstract

The Computer Supported Collaborative Learning

(CSCL) is a paradigm that can be viewed as an evolution

of individual learning done in Intelligent Tutoring

Systems (ITS) and Learning Environments (LE) to a group learning environments. In this paper a Team

Training Strategy (TTS) is decribed, which involves the

use of an Intelligent Collaborative Virtual Environment

(ICVE) that incorporates a Pedagogical Virtual Agent

(PVA) to assist the group during the execution stage. We

are developing an ICVE to support the requeriments of

each phase of the TTS; in this report we describe the

advances and the ongoing work of the ICVE.

1. Introduction

A Collaborative Virtual Environment (CVE) is a

computer-based, distributed, virtual space or set of places;in a CVE people can meet and interact with others, with

agents or with virtual objects [1]. CVEs specially,

Intelligent Virtual Environments [2] can be used to

train one or more students in the execution of a certain

task (IVET: Intelligent Virtual Environment for Training),

particularly in situations in which training in the real

environments is either impossible or undesirable because

it is costly or dangerous.

Training is an activity that promotes in a crossed way

the learning of knowledge in the cognitive domain, as

well as skills in the psycomotor domain; nevertheless,

when it is made with human groups (teams), the scope of

the learning expands to the relational/social domain.Consequently, the instructional-design theories and

models [3] used in the training represent a fundamental

factor for the development of shared cognitive schemes

that can improve the teamwork.

In the following sections we describe the goal of the

study, the TTS, as well as the advances in the

development of the ICVE (CVE + IVET) in order to

comply with the requeriments of each phase of the TTS.

2. Goal of the Study

The goal of the study is to develop a strategy for

training small groups assisted by Intelligent Collaborative

Virtual Environments (ICVEs).In order to assist the training process, the development

of a Collaborative Virtual Environment (CVE) that allows

the human tutor to direct and to coordinate the virtual

meetings of the group is proposed, according to the

activities for some phases of the team train strategy. Also,

the development of a model of a Pedagogical Virtual

Agent (PVA) with the role of group leader in order to

assist the team during the drill and practice activities

(execution) into an IVET is also proposed.

3. The Team Train Strategy (TTS)

The strategy consists of four interrelated stages inwhich the human team to be trained follows an iterative

process of self-assessment about the execution of a plan to

perform a proposed task (see fig. 1).

Figure 1: The Team Training Strategy (TTS)

Integration

Execution

Evaluation

Improvement

Proceedings of the 15th International Conference on Computing (CIC'06)0-7695-2708-6/06 $20.00 2006


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The Integration stage has the purpose of integrating the

human team (using a CVE), as well as providing the

apprentices (assisted by a Human Tutor) with a first

mental schema of the plan to execute for a predefined

task. In the following stage (execution), the team uses an

IVET to execute the planned activities according to their

assigned roles; in this environment a PVA plays a teamleader role to assist the trainees. In the third stage

(Evaluation), the team members will have to evaluate their

previous execution and must identify both individual and

group errors with the purpose of avoiding them in a future

execution stage. Finally, in the last stage (Improvement)

the team members in a virtual meeting co-construct a new

plan (using a planning tool) for the task using as a

baseline the experience acquired during the iterative

execution and evaluation of the initial plan.

The level of members interaction is expected to keep

on growing while the team makes progress through the

strategy stages, and the iterative process followed by the

team will trigger mechanisms that will generate a shared

mental model between its members, enhancing the

collaborative learning and allowing a better group

performance during teamwork.

The domain of the TTS is focused on the operation and

maintenance of industrial equipment, coordinated

manipulation of vehicles and, especially, human rescue

teams for disaster situations. The strategy tries that the

trainees acquire in a crossed way: knowledge, skills, and

attitudes, in the three domains of learning promoted by

training (cognitive, psicomotor, and relational/social).

In order to assist the training process, we are

developing a Collaborative Virtual Environment (CVE)

that allows the human tutor to apply instructional

techniques in virtual meetings with the team, according to

the activities proposed for the phases of Integration,

Evaluation and Improvement.

Also, we have proposed the development of an

Intelligent Virtual Environment in order to assist the team

during the phase of Execution. The Intelligent Virtual

Environment for Training (IVET) will incorporate a

Pedagogical Virtual Agent [4] with the role of group

leader [5]. The Pedagogical Virtual Agent (PVA) will

take as its bases for the tutorial process, the knowledge of

the task, as well as a mechanism for modeling the group

based on the interaction process.

4. The CVE for the Integration Stage

The first stage, Integration, has the purpose of

integrating the human team, as well as providing the

apprentices with a first mental schema about the activities

to execute, and the spatial distribution of the environment.

During this first stage, a human tutor describes the task

to be developed, explains an execution plan and assigns

roles to each team member; in this stage, the human tutor

promotes a favorable social space to induce each

apprentice to his/her integration into the team. The

instructional activities will be applied according to the

technique selected by the tutor [6].

We have proposed a procedure for evaluating

instructional techniques based on fictitious scenarios, and

it has been applied using a prototype to the CVEdeveloped to make the compared analysis of two strategies

of cross-training applied as instructional techniques in the

initiation of human groups (teams) in tasks of

sociotechnical nature [7].

The CVE proposed to support the virtual meeting in

this first stage has been designed to stimulate both the

team communication and the information interchange.

During the meeting, the tutor offers some views of the

plan execution using a virtual board integrated with the

environment. Fig. 2 offers a snapshot of the CVE that we

have implemented [8].

Figure 2: The CVE for the Integration Stage

5. The IVET for the Execution Stage

At the Execution stage, the group members should

achieve the previously described goal, executing the

planned activities according to their assigned roles. To

perform the task, the team will use an Intelligent Virtual

Environment for Training (IVET) which recreates a

scenario in which the apprentices must carry out

sequential or concurrent activities, according to the

established plan.

In order to assist the team during this stage, we

propose to include a Pedagogical Virtual Agent (PVA)

playing the role of team leader. It may communicate with

the apprentices or make them suggestions during the

execution of their activities, if necessary. The PVA will

offer its help to the team, giving preference to activities

that are critical for the task success. Fig. 3 shows a team

members view in an IVET prototype during a training

session.



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Figure 3: Snapshop of the IVET prototype for theExecution Stage

The research team in the Decoroso Crespo Laboratory

at the UPM (Universidad Politcnica de Madrid) has

developed, as part of the MAEVIF Project (Model for the

Application of Intelligent Virtual Environments to

Education) a Software Architecture to facilitate the

development of IVETs [9]. The software architecture has

been devised to be open and flexible, and basically its

composed by two subsystems: the first one deals with the

graphical visualization of the virtual environments and the

interaction with the learners; the other subsystem is a

multi-agent system designed to provide with intelligence

the tutoring system. Both subsystems have been integrated

in an architectural solution that is open and flexible using

CORBA (Common Object Request Broker Architecture),

one of the most used standards for the development of

distributed components.

Simulation Agent

Planning Agent

Tutoring ModelAgent

Individual

CommunicationA ent 1

Graphical and

Interaction Ss,

Graphical and

Interaction Ss,

Interaction

Devices

Interaction

Devices

Action Agent

Trajectory Agent

Learner 1 Learner N

Expert ModelAgent

World ModelAgent

Global Communication

Agent

Student Agent 1

Student Agent n

Student Model

Agent

Individual

CommunicationA ent N

Figure 4: Multi-Agent Subsystem in MAEVIF

The Multi-Agent Tutoring Subsystem (MATS) is a

reinterpretation of the traditional architecture for ITSs

(composed of Expert, Student, Tutoring and

Communication modules) as an architecture based on

cooperative agents [10]. Fig. 4 shows the architecture that

has been implemented.

For the development of the IVET that will be used fordrill and practice in the task (Execution phase) we will

adjust the MAEVIF architecture, according to the

requirements established in TTS, specially, it will be

necessary to extend the MATS with a mechanism to model

the group (Group Model Agent).

Another work line of the research team is the

Pedagogical Agents, intelligent agents who can be

equipped with personal characteristics and emotions to

offer believable behaviors in IVETs.

In our work (ICVET), the incorporation of a PVA as a

team member (leader) with capacity to carry out behaviors

similar to those of a human tutor, will require to integrate

with the IVET a cognitive architecture for the virtual

agent; we will use as a basis for its implementation a

multilayered architecture named COGNITIVA [11]; it has

been designed for virtual agents endowed with personal

characteristics and emotions that offers three possible

layers to the agent designer, each one corresponding to a

different kind of behavior: reactive, deliberative and social

6. The CVE for the Evaluation Stage

At the third stage (Evaluation), the team will have to

self-evaluate their previous execution of the plan. The

human tutor coordinates a virtual meeting of the team to

identify both individual and group errors with the purposeof avoiding them in future plan executions. With this aim,

the team can make use of the CVE to reproduce and

analyze the previous plan execution, focusing on the

achieved performance. The strategy proposes an iterative

cycle of plan execution and evaluation, as many times as it

is necessary to conduct the plan successfully.

We are designing a mechanism to model the group

based on both the domain of the task to be learnt

(cognitive and psychomotor domains) and the group

interaction (relational/social domain). We will use

sentence openers to collect interaction data and construct a

collaboration model [12] accordingly to the TTS, which

will be used by the Human Tutor and by the PVA toprovide the team with support in the collaborative working

(learning) process [13] and in the learning process of

collaborative skills [14]

7. The Planning Tool in the CVE

Finally, at the Improvementstage, the team will have

the opportunity of replanning the assigned activities,

building on the experience acquired by the apprentices



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during the iterative execution and evaluation of the initial

plan. In this stage, the human tutor coordinates the

learners interaction and encourages them to formulate

plan alternatives. To reach this objective, the group uses a

planning tool, provided by the CVE (it will be displayed in

the virtual board).

According to the resulting plan, the execution andevaluation stages will be performed repeatedly, until the

new plan is successfully carried out , leading to a new

improvement stage in which the team can finish their work

or prepare a new alternative plan and keep on training.

The Improvement stage has been designed to provide the

students with a flexible way of thinking about the

proposed activities, allowing them to have a better

adaptation capability to other similar activities in the

future.

We are implementing a planning tool (see Fig. 5)

which incorporates: an editor to co-construct (in a

collaborative session) the plans, a viewer to generate a

PERT Graph of the plan designed for the team, and when

the team consider that the plan is correct, the Builder

generates a plan ontology that can be saved in a repository

of plan-ontologies; other information is saved in a CVE

data base. The World Ontology contains the objects to

locate and actions to perform in a virtual world.

Figure 5: The Planning Tool Structure

8. Conclusions

In this paper, a Team Train Strategy (TTS) as well asthe advances and ongoing work for the Intelligent

Collaborative Virtual Environment to support it has been

described. The team training is an interesting instructional

activity that will be used to research issues in depth both

the collaborative learning process and the learning process

of collaborative skills. We are interested in researching

different theories and models of instruction in the team

training, and we will use and experiment with this TTS

(using the ICVET).

References

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Virtual Environments: Digital Spaces and Places for

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& A.J. Munro (eds) Collaborative Virtual Environments:

Digital Places and Spaces for Interaction. London:

Springer Verlang. 2001. Chapter 1.[2] Aylett, R. & Luck, M. Applying Artificial Intelligence to

Virtual Reality: Intelligent Virtual Environments,Applied

Artificial Intelligence 14 (1). 2000. pp 3-32

[3] Reigeluth, C.M.Instructional-design theories and models:

A new paradigm of instructional theory, Volume II.

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[4] Rickel, J. & Johnson, W.L. Animated Agents for

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[5] Vankeh, K., Ohlsson, S., & Nason, R. Applications of

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[9] De Antonio, A., Ramrez, J., Imbert, R., Mndez, G. &

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[10] De Antonio, A. Ramrez, J. & Mndez, G. An Agent-

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pp. 212-233

[11] Imbert, R. & De Antonio A. When Emotion Does Not

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[12] Soler, A. Supporting Social Interaction in an Intelligent

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[13] Dillenbourg P. What do you mean by collaborative

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An Intelligent Collaborative Virtual Environment forTeam Training — A Preliminary Report