KNOWLEDGE REPRESENTATION

An Ontology for Managing a Virtual Environment

Lydie Edward, Kahina Amokrane

Heudiasyc Laboratory UMR 6599 CNRS, University of Technology of Compi`egne, France

Centre de Recherches de Royallieu, 60200 Compi`egne, France

Domitile Lourdeaux, Jean-Paul Barth`es

Heudiasyc Laboratory UMR 6599 CNRS, University of Technology of Compi`egne, France

Keywords:

Ontology, Virtual environment for training, Multi-agent systems, Knowledge representation.

Abstract:

This paper presents an ontology developed in order to manage a virtual environment for risk prevention. This

ontology represents the objects composing the environment, the agents operating in the environment and the

events that can happened. In the virtual environment, different entities cohabit: virtual operators represented

by cognitive agents and the learner’s avatar that represents a real operator. They can interact with the objects.

It is therefore useful to have on one hand a managing system that well deﬁne the framework in which the

interactions or actions can be allowed and on the other hand a representation of the knowledge involve in

such interactions. To do this, we combine artiﬁcial intelligence and knowledge engineering to propose agent

COLOMBO. It is composed with the developed ontology and a set of reasoning rules.

1 INTRODUCTION

In our work we are interested in virtual environments

with enough credibility that offer human users an at-

tractive platform through which they can gain some

experience for training and decision-making (Edward

et al., 2007). For training and even more for decision-

making, it is necessary to reproduce the criteria/events

that are useful for the operator to decide (for example

if he does not close the gate properly we must have a

leak). We seek to model autonomous virtual charac-

ters interacting with avatars in such environments to

perform a task at a high-risk industrial plant and show

the results of their activity and the risks incurred. It

is therefore useful to have on one hand a world model

and a managing system and on the other hand a repre-

sentation of the knowledge involve in such interac-

tions. Above all existing approaches, we chose to

develop a world model using ontology. The goal of

creating such ontology is to allow users to have ac-

cess to various information (example: what are all

the actions possible on an object? What is the link

between object i and objet j). The representation of

the environment integrates knowledge on the objects

that composed the environment but also on the activ-

ity that agents or avatars have to realized. We com-

bine artiﬁcial intelligence and knowledge engineering

to propose agent COLOMBO

associated with an on-

tology that permits us to represent the working envi-

ronment (objects, agents), actions and events that can

happened in the environment. COLOMBO manages

the actions and interactions with the environment in

the way that it determines if an action is possible and

if all the conditions are satisﬁed to realize the action.

2 STATE OF THE ART

Kallmann proposed Smart Objects that integrate the

informationneeded for creating the character’sbehav-

iors. They provided information are oriented toward

the interaction between the characters and the objects

(Kallman, 2004). He distinguishes several types of

information: (i) intrinsic properties of the object (se-

mantic, physics), (ii) information on interaction (ac-

tions, positions, gestures), (iii) object behavior in re-

sponse of an interaction. In line with the research of

Kallman, Chevallier and Querrec proposed an exten-

sion of UML 2.1 model and developed a metamodel

French Translation : Ontological Creation linked to the

Modeling of the Objects

332

Edward L., Amokrane K., Lourdeaux D. and Barthès J. (2010).

KNOWLEDGE REPRESENTATION - An Ontology for Managing a Virtual Environment.

In Proceedings of the 2nd International Conference on Agents and Artiﬁcial Intelligence - Agents, pages 332-335

DOI: 10.5220/0002735903320335

 SciTePress

called VEHA

. VEHA is used for modeling semantic,

structural, geometrical and topological properties and

agents reactive behaviors (Chevaillier et al., 2009).

3 DEVELOPED ONTOLOGY

The approaches presented above are not sufﬁcient for

our needs. The Smart Objects proposed by Kallmann

ﬁll this lake but do not let the learner to do some er-

rors. The object behaviors are scripted and correct.

The UML approach gives a good framework to spec-

ify relations between objects and actions but it is not

a generic language. Furthermore, it does not gives us

the opportunity to add rules on objects. Thus, in our

approach, we chose ontology as an effective structure

for knowledge representation : “An ontology is an

explicit speciﬁcation of conceptualization ” (Gruber,

1993). Noy and McGuinness deﬁned an ontology as

a formal explicit description of concepts in a speciﬁc

domain, properties of each concept describing vari-

ous features and attributes of the concept and restric-

tions on properties (Noy and McGuinness, 2001). In

addition to these elements, an ontology may contain

different types of relations between its concepts.

Our domain knowledge is situated in the indus-

trial activity on SEVESO sites. To construct the on-

tology, we used knowledge provided by ergonomists.

In addition, knowledge concerning risks are provided

by experts of INERIS

. During the process of on-

tology creation we paid attention to be agree on the

vocabulary. The world domain is different from

the activity domain. We distinguished the ontol-

ogy that is made of the concepts (screw, gate, han-

dle) and the knowledge base that is composed with

the individuals or instances of the different concepts

(screw

5, gate

7, handle

2). We explain in the fol-

lowing the main concepts and relations of our ontol-

ogy (Figure 1).

3.1 Main Concepts

• Object-V3S

The main components of the environment are

the objects. They are represented in the ontol-

ogy by the root concept “Object-V3S” (Figure

2). There is also subconcepts such as: opening-

object (valve, door), container-object (toolbox,

cupboard), tools (screwdriver, hammer, Swiss

army knife). Each real object in the environment

is represented by an instance in the knowledge

Virtual Environment supporting Human Activities

Industrial Environment and Risk National Institute

base. During a working session, the state of a

given object may change from a value to another

(opened, shut, taken) and its decay characteris-

tic (normal, rusted, broken) may change also, and

thus we added respectively the attributes “state”

and “status”. At each moment, an object has one

or several states but only one status. An object is

also described by its position and its orientation

in the world. We distinguish several categories of

objects according to their function and utility. If

the object is linked to another object (i.e a door

and its handle) we called it a cognitive object oth-

erwise it is a reactive object.

• Action-V3S

Each autonomous agent or learner may perform

one or more tasks on objects in the virtual environ-

ment. We have thus added the “Action-V3S” con-

cept that represents all the actions that can be per-

formed in the environment(open, close, unscrew).

These actions are classiﬁed according to the ob-

jects on which they can be applied. We have for

example the subconcepts: valve-action, loading-

arm-action. This distinction is useful to abstract

the relations between concepts. During the exe-

cution of an action, it changes from one state to

another. It can be current (active), awaiting (pend-

ing), ended (ﬁnished) or in failure (failed).

• Agent

To represent the work team (team leader, operator,

manager) at a site, we added the “Agent” concept.

Team members are represented by autonomous

virtual agents and the avatar of the learner in the

virtual environment. We have different categories

of agents according to their function. It corre-

sponds to the following subconcepts: Operator,

Manager and Project Manager. An agent has a po-

sition and orientation in the environment, a tool-

box containing its tools and an equipment box

with its protection clothes.

3.2 Ontology Relations

In addition to the classical subsumption relation be-

tween concepts, we enriched our ontology with other

(horizontal) relations. We describe below some of

these relations :

• “has-main-resource” relation between an action

and an object. It speciﬁes the appropriated objects

needed to realize the action. For example, the ac-

tion ”loosen” on the object ”screw” has for main

resource the object ”screwdriver”.

• “has-secondary-resource” relation between an ac-

tion and an object. It speciﬁes the other objects

KNOWLEDGE REPRESENTATION - An Ontology for Managing a Virtual Environment

333

that can be used to realize the action. For exam-

ple, the action ”loosen” on the object ”screw” has

for secondary resource a sort of screwdriver such

as a ”Swiss army knife”.

• “has-target” relation between an action and an ob-

ject. It means the instance of the object concerned

by the action.

• “has-main-action” : relation between an action

and an object. Main actions are the tools main

function (i.e screwdriver : unscrew, screw).

• “has-in-hand” : relation between an agent and an

object. It speciﬁes which tools are in the agent

hand.

The relation has-secondary-resource between an

object and an action is created automatically at the

loading of the knowledge base. We added a rule-

based system that identiﬁes according to certain con-

straints, the objects to link with an action. For exam-

ple all the objects with a certain weight and geometry

will be secondary-resource-of the action knock. The

rules are speciﬁcally based on the objects properties.

This system is very helpful to maintain the ontology.

If we add some new concepts, with their attributes and

properties we do not need to specify for which action

they can be secondary resource.

Figure 1: Ontology.

3.3 Implementation

Our ontology has been constructed with MOSS

(Barth`es, 2009), a modeling language implemented

with the Lisp language. MOSS has a powerful engine

that permits to access to the entire ontology and the

knowledge base through simple or complex queries.

The query system takes a formal query as input, fo-

cusing on a particular concept : the question ”Per-

sons whose name is Dupond” gives the query (”per-

son” (”name” :is ”Dupond”)). It then computes a set

of possible candidates, and tries to validate each can-

didate in turn. Creating a concept is simple and can

be done with the defconcept macro as follows: (de-

fconcept ”Gate”). MOSS objects have two kinds of

properties: immediate values that qualify the object,

and links to other objects. The ﬁrst kind is called an

attribute (:att), the second kind is called a relationship

or relation (:rel).

Figure 2: Concept Object-V3S.

4 RULES

We do not aim at only describing the objects and

actions. But we want to let the learner and the

autonomous agents to do some errors (pedagogical

choice). But we avoid the errors that can not be done

in real life. For example to unscrew with a hammer.

It is not possible. Thus, if the learner or the agent

decides to do an action, it does not mean that the ac-

tion will be done. For example, if the agent wants to

close the gate and the gate is already closed, the ac-

tion will not be done (no changing state). Thus we

add information into the objects and also into the ac-

tion in order to manage how an object can change its

state and when an action can be executed. We distin-

guish two kind of objects, reactive objects (screw) and

cognitive objects (loading-arm). The reactive objects

are just like reactive agent, they respond directly to a

certain stimuli (an action). For example, if the learner

decides to unscrew the screw, the result of the action

will not depend on other parameters than the con-

cerned object state. The cognitive objects are more

complex as cognitive agents (Demazeau, 1995) (Fer-

ber, 1995). For example, let us take the loading-arm.

There is a link called ”connect” between the concept

loading-arm and the concept gate. In order to remove

the loading-arm, we have to be sure that the loading-

arm’s handle is turned and that the appropriated gate

is unlocked. To manage these different situations, we

add two kind of rules into the cognitive objects and

into the actions : execution rules and transition rules.

• Execution rules

ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence

334

In response to the agent or learner actions the en-

vironment should be able to respond correctly. If

the agent wants to do an action we need to know if

this action is possible. This role is done by agent

COLOMBO. When the agent or the learner wants

to do an action, COLOMBO receives a query.

Then it veriﬁes the execution rules linked to the

action (Figure 3). If the conditions are satisﬁed

then the action is possible and an animation is

started in the environment.

Figure 3: Concept action unscrew.

• Transition rules

The transition rules are the conditions that permit

an object to change its state. They are stored in the

object concept (Figure 4). As we said above, the

realization of an action does not necessary implies

that the target object change its state. Thus, only

if they transition rules are satisﬁed, COLOMBO

sets the new value of the object state.

Figure 4: Concept object loading-arm.

5 CONCLUSIONS

In this paper we present the development of an ontol-

ogy for managing a virtual environment. Our ontol-

ogy is not only a formal description of the concepts

of a domain but we also aim at building a intelligent

system that decides : (i) if the action is possible, (ii)

if the object can change its state. To reach this goal,

we proposes two kind of rules : (i) execution rules

deﬁned in the action concept and (ii) transition rules

deﬁned in the cognitive object concept. The next step

of our work will be to enrich the system and to found

solutions for the maintenance of the ontology.

ACKNOWLEDGEMENTS

Authors want to thank the ergonomists of the Univer-

sity of Paris Descartes; INERIS for providing the nec-

essary information. We also thank the persons who

worked with us on the development of the ontology

and the virtual environment.

REFERENCES

Barth`es, J.-P. (2009). OMAS - A Flexible Multi-Agent En-

vironment for CSCWD. CSCWD.

Chevaillier, P., Querrec, R., and Septseault, C. (2009). Veha

: Un m´eta-mod`ele d’environnement virtuel inform´e

et structur´e. Revue des Sciences et Technologies de

l’Information, s´erie Techniques et Sciences Informa-

tiques (RSTI-TSI).

Demazeau, Y. (1995). From interactions to collective be-

haviour in agent-based systems. European Conference

on cognitive sciences.

Edward, L., Lourdeaux, D., Lenne, D., Barth`es, J.,

Burkhardt, J., and Camus, F. (2007). V3S: A Training

and Decision Making Tool to Model Safety Interven-

tions on SEVESO Sites. VRIC.

Ferber, J. (1995). Les syst`emes multi-agents - vers une in-

telligence collective. Paris : InterEditions.

Gruber, T. (1993). A translation approach to portable on-

tology speciﬁcations. Knowledge acquisition, 5:199–

220.

Kallman, M. (2004). Interaction with 3-d objects. In

Magnenat-Thalmann, N. and Thalmann, D. editors,

Handbook of Virtual Humans, chapter 13, pages

303322.

Noy, N. and McGuinness, D. (2001). Ontology develop-

ment 101 : A guide to creating your ﬁrst ontology.

Technical Report KSL-01-05 and SMI-2001-0880.

KNOWLEDGE REPRESENTATION - An Ontology for Managing a Virtual Environment

335