INTELLIGENT VIRTUAL ENVIRONMENTS FOR TRAINING IN

NUCLEAR POWER PLANTS

Gonzalo M

endez, Pilar Herrero, Ang

elica de Antonio

Computer Science School, Technical University of Madrid

Campus de Montegancedo s/n, 28660 Boadilla del Monte (Madrid), Spain

Keywords:

Intelligent Tutoring System, Pedagogical Agent, Virtual Environment, Training, Software Architecture, Nu-

clear Power Plant.

Abstract:

Educational Virtual Environments are gaining popularity as tools to enhance student learning. These environ-

ments are often used to allow students to experience situations that would be difﬁcult, costly, or impossible in

the physical world. At the Technical University of Madrid we have developed several applications to explore

the use of intelligent tutors in VR. In this paper we present two of these applications which have been used for

training in radiological protection in Nuclear Power Plants (NPP). These applications are inhabited by avatars

and/or agents which are continuously monitoring the state of the environment and manipulating it periodically

through virtual motor actions. Our applications help students learn to perform physical, procedural tasks in

some different risky areas of NPP.

1 INTRODUCTION

Virtual Reality (VR) and Virtual Environments (VE)

can provide us with simulation-based learning envi-

ronments, offering exciting opportunities and chal-

lenges for educational software and for intelligent tu-

tors. As in any simulation-based learning environ-

ment, students may reach impasses or fail to recog-

nize learning opportunities, so they can beneﬁt from a

computer tutor that can provide answers to their ques-

tions and offer advice. Currently, there are mainly

two kinds of intelligent tutors that are being used to-

gether with VEs: Intelligent Tutoring Systems (ITS)

(Sleeman and Brown, 1982) and pedagogical agents

(Johnson et al., 2000), some of which are discussed

in section 5.

VEs offer a broader ﬂexibility for human-computer

interaction than earlier technologies did. First, the

computer tutor can inhabit the virtual world along

with students, which allows a wider variety of interac-

tions between students and tutors. Second, VR allows

the tutor to track students’ visual attention and phys-

ical movements (e.g., the position and orientation of

their hands). Thus, VR opens up new possibilities for

teaching physical tasks.

VEs are especially valuable in domains where real-

life training is very expensive or where students can

experience some risky situations, such as mainte-

nance or control of Nuclear Power Plants (NPP) (Pan-

telidis, 1996). In addition, VR can support more be-

lievable stimuli and reactions than earlier technolo-

gies, thereby providing an adequate simulation for a

wider range of situations.

To explore the use of intelligent tutors in VR, we

have developed two applications for training in NPPs:

PRVIR (Virtual Reality Technology applied to Train-

ing in Radiological Protection) (Mendez et al., 2001)

and MAEVIF (Model for the Application of Intelli-

gent Virtual Environments to Education and Train-

ing) (de Antonio et al., 2003). Our applications help

students learn to perform physical, procedural tasks,

such as the procedure for admission in a NPP or the

entrance in a radiologically controlled area.

The PRVIR application was developed for training

operators of Nuclear Power Plants (NPP) in radiolog-

ical protection.

PRVIR is divided into two sections. The ﬁrst one

teaches the operators the concepts related to radiology

and radiological protection: what radiation is, types

of radiation, procedures, etc. It is designed as a mul-

timedia course, where the student has to learn some

concepts and pass an exam before he can advance in

the course.

In the second section, the student is shown how he

204

Méndez G., Herrero P. and de Antonio A. (2004).

INTELLIGENT VIRTUAL ENVIRONMENTS FOR TRAINING IN NUCLEAR POWER PLANTS.

In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 204-209

DOI: 10.5220/0002642002040209

 SciTePress

must act inside the plant, and then he has to perform

inside a VE the procedures he has seen (see Figure 1).

Figure 1: A sample VE.

The ITS monitors the student’s actions inside the

VE, giving hints when these actions are not correct.

This paper begins describing the ITS we have used

in the PRVIR application (section 2) and the archi-

tecture for its integration with the VE (section 3); it

then presents our current and future work (section 4)

and it analyses some related work in the area (section

5); ﬁnally, it details some of the conclusions we have

obtained (section 6).

2 STRUCTURE OF THE ITS

The ITS we have developed consists of four mod-

ules, each of which provides a very precise func-

tionality: tutoring module, expert module, student’s

module and communication module, as originally de-

scribed in (Wenger, 1987).

The Expert Module contains the knowledge about

the subject to be taught to the student, and it is the

base for the analysis of the answers provided by the

student to the tutor’s questions. This knowledge is

divided into informative concepts, which are small

pieces of information, and the knowledge necessary

to solve the exercises. The expert module must be

designed in a way that information is easily accessi-

ble and modiﬁable. The way we have overcome this

problem is saving all the information in a relational

database. Each informative concept points to other

concepts that must be shown necessarily before or af-

ter it, as they are all part of a particular block of con-

cepts. Each block, in turn, points to other blocks that

must be shown necessarily before or after it within a

given module.

The Tutoring Module contains the pedagogic

knowledge, and is in charge of selecting the appro-

priate concepts to be shown in the course. It also has

the strategies, rules and processes needed to drive the

interactions between the student and the system, in or-

der to make decisions about the concepts to teach and

the exercises to be done by the student, along with

the moment when he must be interrupted in order to

correct him or make a suggestion. In addition, it de-

cides when it is appropriate to end showing informa-

tive concepts and start with an evaluation. At the end

of each block of concepts, there is a bunch of exer-

cises related to the concepts explained in that block,

so the student can test his recently acquired knowl-

edge. There is also an evaluation at the end of each

module, where exercises from all the blocks that form

that module will be chosen.

The Student’s Module keeps individualized infor-

mation about every student taking the course. It is re-

sponsible for tracing what informative concepts have

already been taught to the student, how many exer-

cises he has done and the degree of success and time

he has used to complete them. To measure the stu-

dent’s progress, we need some metrics with which to

compare what are the minimum and average levels for

a student to pass to the next level.

The Communication Module is in charge of the

communication between the student who is taking the

course and the ITS. This module must inform the tu-

toring module about the actions that are performed by

the student all along the course. These actions may

be the visualization of an informative concept, the an-

swering to an exercise in any of the forms that it may

adopt, or any of the actions performed inside the VE.

The communication module must make use of all the

available multimedia resources in order to make the

course as easy-going as possible, but ensuring it does

not make the course be too slow, which in the end

could bore the student.

Each of the described modules has a very important

role to play in the correct operation of the ITS.

3 ARCHITECTURE

The architecture is the key issue in the correct func-

tioning of the system we have just described. While

doing so, we have provided some clues on how this

integration may be done. Now, we will explain in de-

tail how this has been done in our system.

From the former section we can infer the following

information:

• The communication module must inform the ITS

about the actions of the student.

• From the point of view of the tutoring module,

there are two different types of exercises. One of

them includes the situations simulated in the VE,

and the other one encompasses all the different ex-

ercises related to the basic concepts.

INTELLIGENT VIRTUAL ENVIRONMENTS FOR TRAINING IN NUCLEAR POWER PLANTS

205

This means that we can consider the VE as a par-

ticular kind of exercise and that the actions performed

in it must be supervised by the tutoring module.

Thus, the structure of the integrated application is one

shown in Figure 2.

Figure 2: Architecture of the integration.

Once the general structure of the application has

been decided, it is necessary to design the way the

tutoring module is going to be able to supervise the

actions that take place inside the VE.

The steps to be followed inside the NPP are very

well deﬁned, and an action cannot be carried out if the

ones that go before that one haven’t been performed

already. Thus, the representation of a procedure using

state diagrams, where the actions that make the state

change are the actions that the student can do inside

the VE, seems to ﬁt perfectly our needs (see Figure

3). In some situations, there may be different pos-

sibilities in which several actions can be performed

in a not predeﬁned order. In this case, the state dia-

gram may have some branches, but they will eventu-

ally converge in a state from where the process will

continue.

When these diagrams are designed, the most ﬂexi-

ble way to implement the tutoring module is to build

a general mechanism that is able to read the structure

of any state diagram as well as the actions that must

be done to change from one state to another. The state

diagram, as we have already done with the rest of the

expert module, is stored in a relational database.

The last problem to be solved has to do with the

communication between the VE, the communication

module and the tutoring module. An action inside the

VE might not be possible due to the following two

reasons:

• The selected action may not be the right action to be

performed at that moment, either because it is not

to be done at that point in the sequence of actions

or because it is not part of the procedure. This de-

cision must be taken in terms of the expert knowl-

edge.

• It may not be possible to physically do the action,

Figure 3: A sample scene.

usually because the object is too far to be able to in-

teract with it. This has to do with restrictions inside

the VE, and not with the ITS.

Because of these two sources of problems, when

the student tries to do something inside the VE, the

procedure to be followed is:

1. The communication module asks the VE if it is

physically possible to do that action.

2. If it is possible, then it asks the tutoring module if

it is the right action to be done in that moment.

3. The tutoring module asks the expert module if it is

the right action to be done in that moment.

4. If it is the right action, then the tutoring module

tells the VE to perform the action.

5. If it is not possible to do the action or if it is not

the right action, then the communication module

shows a message to the student. This message is

sent by the tutoring module in case the action is

wrong, or by the VE if it is not physically possible

to do that action.

4 CURRENT AND FUTURE

WORK

PRVIR, although successful, has some serious restric-

tions imposed by our clients at the NPP, and they

mainly have to do with the tutor not letting the stu-

dent deviate from the predeﬁned procedures, so that

he can explore other possibilities, or with the adapta-

tion of the tutor to the student’s needs.

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206

Thus, the tutor in this application is not very intel-

ligent or skilled, and, as an additional drawback, it

is not embodied, so it cannot demonstrate the student

how to perform the procedure he is studying.

Even so, the PRVIR system is already in use at the

Vandellos II NPP in Spain. Despite the limitations

we have mentioned, this has been the ﬁrst step to use

VR in a Spanish NPP to train operators in different

tasks, so depending on how successful it is, we will

be adding more capabilities to the intelligent tutor, so

that in a close future these system can effectively be

used instead of the training inside the NPP.

Several experiments have shown that the learning

experience is much more effective if there is an em-

bodied tutor inside the VE helping the student in the

training process (Lester et al., 1997a). Because of

this reason, we are currently developing a new sys-

tem, MAEVIF, where we are substituting PRVIR’s

ITS with a multiagent system that will have analogous

functions, but that will support an embodied tutor and

will also be easier to expand and modify.

MAEVIF (Model for the Application of Intelligent

Virtual Environments to Education and Training) is

a project funded by the Spanish Ministry of Science

and Technology. The objective of the project is the

deﬁnition of a model for the application of intelligent

virtual environments in education and training. This

implies:

• The deﬁnition of a generic model for intelligent

learning environments based on the use of virtual

reality.

• The deﬁnition of an open and ﬂexible software ar-

chitecture to support the generic model of a learn-

ing environment.

• The design and implementation of a prototype au-

thoring tool that simpliﬁes the development of

learning environments based on the generic model.

• The deﬁnition of a set of methodological recom-

mendations for the development of virtual learning

environments. This methodology will propose a set

of steps for the design of the environment accord-

ing to the generic model, and its implementation

with the help of the authoring tool.

The most relevant aspects that are being taken into

consideration in the deﬁnition of the generic model

are:

• The design of a tutoring strategy, easily adaptable

and conﬁgurable, that allows for an intelligent su-

pervision as well as for the dynamic adaptation of

the system to the special needs of each student.

• The design of a cognitive diagnosis method which

is able to perform non-monotonic reasoning about

the knowledge of the student and their individual

characteristics.

• The deﬁnition of a knowledge representation for-

malism for the expert knowledge which is specif-

ically crafted for the use within virtual environ-

ments, and which is independent of the language

used for its development.

Each component of the architecture of the intelli-

gent tutor is an agent with a very speciﬁc function,

the most relevant of which are:

• Planning agent: it uses a strips-based algorithm

to plan and replan what the students have to do.

This makes it possible to dynamically generate ex-

ercises, so that students can practice different pro-

cedures starting from different situations.

• Tutor agent: according to the planning agent’s plan,

this agent is in charge of explaining the student

what he has to do, giving hints, supervising him

or showing the appropriate actions.

• Plath-planning agent: this agent is in charge of cal-

culating the appropriate routes to go from one place

to another inside the VE using the RTA* algorithm.

It also controls the student’s movement to see if he

is following the right path. If not, it gives hints to

the student so that he can follow the right route.

• World agent: its mission is to keep a snapshot of the

state of the world, so that he has all the information

about the world that other agents may need. This

information includes the position of all the objects

and avatars, the state of different devices or the ob-

jects that each student is carrying.

The communication among agents is carried out

using a blackboard. Every time an agent needs some-

thing, it publishes it on the blackboard. The rest of

the agents will see if they have something useful, and

will write it on the blackboard, too. The original agent

will then see all the options and choose the one that

best suits its needs.

This mechanism has been used, for example, by the

planning agent. This agent publishes the state of the

plan, and asks the rest of the agents if they can con-

tribute something to the plan. With all the answers,

the planning agent explores the different alternatives

until the plan is ﬁnished.

MAEVIF supports several students being trained

at the same time using different computers connected

though a network.

The agents are being developed using JADE, and

the VE has been developed using OpenGL. The con-

nection between both of them is based on CORBA,

so, as an additional feature, they can run in different

machines to improve the system’s performance. Fi-

nally, the connection between different clients is done

using DirectPlay, so the visual state of the VE is con-

sistent in all the clients.

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207

5 RELATED WORK

There are several projects aiming at the use of VR for

education and training.

In all probability, the most famous embodied peda-

gogical agent nowadays is Steve(Soar Training Expert

for Virtual Environments). Steve is an autonomous,

animated agent for training in 3D virtual environ-

ments (Rickel and Johnson, 1999) that has been de-

veloped at USC. Steve’s role is to help students learn

procedural tasks, and he has many pedagogical ca-

pabilities one would expect of an intelligent tutoring

system: Steve can demonstrate procedures, it can

monitor students as they practice a task, giving them

feedback on their actions, and it can answer simple

questions.

Steve was designed to be easy to use in new do-

mains and virtual worlds. It was originally applied

to equipment operation and maintenance training on

board a virtual ship. Subsequently, it was signiﬁcantly

extended and applied to leadership training in virtual

Bosnia (Rickel et al., 2002).

However, the leadership training application was

designed with Steve in mind. We have recently car-

ried out an experiment to integrate Steve in an al-

ready existing VE (Mendez et al., 2003), and although

it worked quite well, some issues did arise that re-

quire further consideration in the design of this kind

of agent.

Adele (Agent for Distance Education - Light Edi-

tion) (Shaw et al., 1999) has also been developed at

USC. The functionality of Adele is quite similar to

that of Steve, but it has been extended to support some

additional persona features and instructional capabil-

ities that Steve lacks. In addition, whereas Steve has

been thought to be used for training, Adele has been

mainly designed to be used in education.

Adele has been used in a case-based clinical diag-

nosis application, and she can highlight interesting as-

pects of the case, monitor and give feedback as the

student works through a case, provide hints or ra-

tionales for particular actions, or quiz the student to

make sure he understands the principles behind the

case.

Cosmo is a life-like animated agent developed at

NCSU IntelliMedia Initiative (Lester et al., 1997b).

Given a request for an explanation or a hint, Cosmo’s

behavior planner selects the explanation, which is

mainly determined by the current problem state.

Then, the explanation planner consults the knowledge

sources to select a sequence of communicative acts.

To ease the student’s acquisition of problem-solving

skills, the explanation planner supplies him with rel-

evant causality knowledge when the student requests

advice, as well as justiﬁcations for its suggestions.

A lot of effort has been spent to endow this agent

with diectic believability, which allows him to move

through the environment, point to objects and refer

to them appropriately. For that, a diectic behavior

planner is used to coordinate locomotive, gestural and

speech behaviors.

Herman the Bug is another life-like pedagogi-

cal agent developed at NCSU IntelliMedia Initia-

tive (Lester et al., 1999) that has been used in the

DESIGN-A-PLANT learning environment. The ob-

jective is for students to learn concepts about botani-

cal anatomy and physiology.

Herman is an insect that dives into plant structures

and provides problem-solving advice to students. As

students build plants, Herman observes their actions

and provides explanations and hints.

This learning environment was built in order to

study mixed-initiative problem-solving interactions in

constructivist learning environments. Herman cannot

participate in complex dialogues requiring turntaking,

back channeling, or even rudimentary discourse seg-

mentation. The authors have been able to identify

what they have called the persona effect (Lester et al.,

1997a), meaning that the presence of a lifelike charac-

ter in an interactive learning environment - even one

that is not expressive - can have a strong positive ef-

fect on student’s perception of their learning experi-

ence.

Vincent is a synthetic pedagogical agent that helps

the trainees in the web based learning process (Paiva

and Machado, 2002). This agent combines a set

of sensors and actors that establish message-based

communication with the micro-learning environments

while gathering information about trainee perfor-

mance. It has an anthropomorphic representation fea-

turing four emotional attitudes: sad, happy, disap-

pointed, and impatient.

Vincent can perceive the environment and act on

it, having the capability of making inferences about

those perceptions, solving problems and determining

what actions should be performed to reach his goals.

He can do this through his cognitive behavior, which

implies deciding what pedagogical actions must be

taken for a particular situation, and his physical be-

havior, which includes Vincent’s visual and audio at-

titudes.

The use of pedagogical agents seems to be more

extended than ITSs, due to the beneﬁts obtained from

their embodiment inside VEs, and this tendency will

increase when these agents are commonly endowed

with human attributes such as perception, personality

traits, natural language recognition and generation or

ergonomical restrictions, since training will be much

more realistic.

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208

6 CONCLUSIONS

Although secure, Nuclear Power Plants are a very

special environment where all precautions and help

are always welcome. As we learned during the de-

velopment of the PRVIR project, there are certain ar-

eas where human presence is not advisable. However,

from time to time it is necessary to inspect their state,

and in these cases, the better the action is planned and

trained, the less dangerous it is for the person who has

to perform it.

For these reasons, NPPs will be really beneﬁted

from the advance in the development of VEs as a sub-

stitute for physical mockups of the plant, since they

constitute a more economical solution for planning

and training than former ways do.

Our ﬁrst experience adding intelligent tutoring in

the PRVIR project has been quite satisfactory, and al-

though the system had some limitations, it provided

us and the Vandellos NPP with a very valuable expe-

rience to carry on with this work.

As a result of this experience and our previous work

with ITSs and agents, we expect the MAEVIF system

to be a much more sophisticated substitute for VEs

for training with intelligent tutoring, in which it will

be possible to substitute any of its components with

a different one in order to better adapt the system to

the particularities of each domain and user, as well

as to take advantage of new advances in science and

technology.

For this to be possible, it would be desirable that

all the researchers and developers of this kind of sys-

tems worked towards the elaboration of standards that

allowed the construction of interchangeable compo-

nents. These standards will be quite beneﬁcial for

the development of VEs for training, due to the wide

range of disciplines involved in the development of

these systems and the difﬁculty to have experts in all

of them in the development teams.

Unfortunately, as far as we can see, these standards

are still far from being available.

ACKNOWLEDGEMENTS

PRVIR was funded by the Electrical Group for the

Nuclear Technological Development (DTN). MAE-

VIF is funded by the Spanish Ministry of Science and

Technology under contract TIC2000-1346.

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