APPLYING FIPA STANDARDS ONTOLOGICAL SUPPORT

TO INTENTIONAL-MAS-ORIENTED UBIQUITOUS SYSTEM

Milene Serrano and Carlos José Pereira de Lucena

Department of Informatics, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil

Keywords: Ubiquitous Computing, Intentional Systematic Software Development, Intentional Multi-Agent Systems,

Ontology, Knowledge Representation, Interface Dynamic Construction, Reuse.

Abstract: In this paper, we present the development of an Intentional-MAS-Oriented Ubiquitous System driven by the

FIPA Standards Ontological Support. This support contemplates the development with a certain degree of

commonality. Our main goal is to improve the Intentional Systematic Software Development for further

Ubiquitous Systems, by considering the same language, vocabulary, and protocols in the agents'

communication and inter-operability as well as an adequate context-aware knowledge representation for

different smart-spaces.

1 INTRODUCTION

Ontology consists of a knowledge formal

representation – a specification of conceptualization

(Staab and Studer 2004.) We commonly use

ontology in order to specify types of

conceptualizations, obtaining a simplified version of

the real world based on the concepts of the domain,

and the relationships between these concepts.

Particularly, Ubiquitous Computing must deal with

the communication among different devices, smart-

spaces, and people, which are distributed (Weiser

1991) (Bell and Dourish 2006). In this scenario, it is

relevant and intuitive the use of an ontological

support to improve the inter-operability among

different smart-spaces and their entities as well as to

consequently standardized the communication

between them, and the knowledge sharing in their

social/interactive events.

In the last years, we have different research

groups that investigate the ontology use in

ubiquitous and pervasive systems (Masuoka et al.

2003) (Chen et al. 2003), proposing interesting

solutions to specifically handle ubiquitous issues:

In (Christopoulou and Kameas 2005), the GAS

Ontology describes the semantic of some concepts

and their interdependencies based on ubiquitous

environments. The authors also provide a common

language for the communication involving different

devices, and a service discovery mechanism. The

goal of the GAS Ontology is to deal with the

semantic inter-operability among heterogeneous

eGadgets and the semantic service discovery.

Finally, the authors discuss about the GAS Ontology

manager. This support runs on each device, by

managing its ontology and processing the

knowledge that each device needs over time.

In (Ranganathan et al. 2003) and (Ranganathan et

al. 2004), the authors present the integration of

ontology and Semantic Web technology into their

pervasive computing infrastructure, the GAIA

smart-spaces. According to Rangnathan et al. the

focus of their work was in three main issues:

Discovery and Matchmaking; Inter-operability

between different entities; and Context-awareness.

Moreover, the approach followed by GAIA

combines different kinds of ontology, divided in two

major groups: ontology for different entities, and

ontology for context information. Based on their

experimental work, the authors argue the relevance

of ontological support to improve the development

of pervasive environments, by overcoming

challenges commonly found in pervasive contexts.

For example, augmenting the system services, which

includes configuration management; human

interfaces, components interoperation, and context

sensitive behaviour.

Considering some deficiencies presented on (Ye

et al. 2007) that must be addressed in order to

successfully apply ontological support to the next-

generation systems in Pervasive Computing and

Ubiquitous Computing; and also our Ubiquitous

114

Serrano M. and José Pereira de Lucena C. (2010).

APPLYING FIPA STANDARDS ONTOLOGICAL SUPPORT TO INTENTIONAL-MAS-ORIENTED UBIQUITOUS SYSTEM.

In Proceedings of the 12th International Conference on Enterprise Information Systems - Software Agents and Internet Computing, pages 114-121

DOI: 10.5220/0002969001140121

 SciTePress

Computing Group needs, we are focused on multi-

agent communication field. Moreover, we are

concerned in ontological models for intentional

multi-agent systems (MASs).

Intentional MAS paradigm is an emergent

technological support, in which the “like me”

recognition (Gordon 2005), goal formation (Dignum

and Conte 1998), and Belief-Desire-Intention (BDI)

Model (Bratman 1987) (Georgeff et al. 1998)

(Pokahr et al. 2005) (Bigus and Bigus 2001) are

intrinsic and intense. An Intentional MAS represents

an adequate support to achieve explicit ascription of

mental states; an essential feature to guarantee

autonomy; and a respectable philosophical model of

human practical reasoning. Thus, it poses some

novel challenges to deal with the communication

and inter-operability among cognitive agents;

between the agents and the ubiquitous environments;

and between the agents and the final users.

Unfortunately, we have few research groups that

consider ubiquitous systems driven by Intentional

MAS in order to, for example: (i) manager different

smart-spaces and heterogeneous access devices

using the intentional agents’ rationale; and (ii) deal

with the user’s satisfaction based on belief-desire-

intention-oriented support, and interface dynamic

construction. Furthermore, if we consider the use of

ontology in the intentional-MAS-oriented ubiquitous

development, it is really difficult to find work that

fills this gap. This technological gap motivated us to

explore an ontological support to deal with the

communication among different intentional agents.

In order to achieve our goals, we applied our

ontological support to validate an extensive

ubiquitous dental case study. Our experimental

research in the Software Engineering Laboratories at

PUC-Rio and UofT demonstrates that this

ontological support offers adequate resources to the

developing of Intentional MAS in ubiquitous

contexts, by considering some important concerns:

(i) users’ intentionality, (ii) changeable contexts, (iii)

devices heterogeneity (e.g. limited or not; and

mobile or not); (iv) distributed smart-spaces; and (v)

service omnipresence using a stable communication

protocol based on the FIPA Coder and Decoder

classes for SL Language. Moreover, the support can

be reused and specialized for various ubiquitous

applications in different cognitive domains.

The paper is organized in sections. Section 2

presents the use and the validation of the ontological

support in a ubiquitous dental system development

centered on Intentional MAS. Section 3 reports on

the lessons learned. Section 4 is dedicated for the

concluding remarks. Finally, Section 5 describes

further work.

2 APPLYING ONTOLOGY

As previously mentioned, we are using ontology in

order to improve the Intentional Systematic

Software Development for Ubiquitous Systems –

ISSD for UbSystems (Serrano et al. 2009.) In

ubiquitous contexts, in which the interaction

between different smart-spaces is intrinsic, it is

interesting that the agents communicate with other

agents by considering the same language,

vocabulary, and protocols. Moreover, when the

communication and the contents involved into this

communication are standardized, the same

represented knowledge can be easily shared and

reused by ubiquitous applications in different

cognitive domains.

As we are following the FIPA standards defined

in the JADEX Framework (Braubach et al. 2004)

and the JADE-LEAP Platform (Caire 2003), our

Intentional MAS already supports certain degree of

commonality. This Intentional MAS specifically

uses the FIPA Coder and Decoder classes for SL

Language (Bellifemine et al. 2007). This language

standardized the messages exchanged among the

agents of the platform. However, we observed that it

would be appropriate to define the agents own

vocabulary and semantic to adequately deal with the

contents of the exchanged messages. In other words,

we must define a specific ontology for this scenario.

We have different ways to define the ontology.

We decided to use the FIPA SL Codec to do this.

Thus, it involves the definition of the elements,

which will be transferred into the messages as

extension of predefined classes. The main idea is to

describe the elements that compose the exchanged

messages. The FIPA SL Codec can encode and

decode these messages using the FIPA format,

allowing the communication among intentional

agents in different smart-spaces.

According to the FIPA SL Codec, the ontology is

composed of the vocabulary and the nomenclature.

The vocabulary describes the concepts terminology.

These concepts are used by the agents in the

interaction among them. The nomenclature describes

the concepts semantic and structure, and depends on

the relationships among these concepts.

Figure 01 (SADT) shows the construction

process of our ontology. It includes some activities

from the State-Of-The-Art investigation – as well as

the experimental work conduction in the Software

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Figure 1: Our ontology construction.

Engineering Laboratories at PUC-Rio and UofT for

defining, registering, creating and manipulating the

ontology – to the results analysis, generating the

lessons learned and further work ideas.

Only to elucidate, we can consider a specific

scenario based on an extensive dental case study,

developed using our ISSD for UbSystems – briefly

described on (Serrano et al. 2009).

Dental Scenario Description: A patient wants to

be registered in a dental clinic. Her/His personal

agent, which is inside her/his device, requests the

registration. The clinic agents receive the request

and send the registration form.

The first challenge in this scenario is that every

decision is made by the agents at runtime. Moreover,

the sent form depends on the request, user’s

preferences (e.g. privacy policies), devices features

(e.g. interface limitations and memory and

processing capacities), network specifications, and

contract information. Thus, a pre-defined

communication protocol must be considered in order

to avoid further disagreements and mistakes. The

communication is standardized using ontology.

In order to implement the ontology for this

scenario, we had to extend the classes

BasicOntology and ACLOntology, predefined in the

FIPA SL Codec, by adding the elements schemas

that describe the structure of the concepts, agent

actions, and predicates of the exchanged messages.

The Concept, AgentAction, and Predicate are

interfaces, which correlated classes are

ConceptSchema, AgentActionSchema, and

PredicateSchema. In fact, these interfaces have a

super-class called ObjectSchema. As follows, we

have a brief description of Concept, AgentAction,

and Predicate:

• Concept represents expressions that indicate

entities with a complex structure, such as:

(Patient :id 000000 :name Mary :address "111

Something Street"). It means that there is a

patient with the id 000000, the name Mary, and

the address 111 Something Street.

• AgentAction represents concepts that indicate

actions performed by the agents in the multi-

agent systems platform, such as: (Request

(Registration :clinic "Dental Clinic ABCD")

(Patient :id 000000)). It means that the patient

with the id 000000 requests the registration for

the Dental Clinic ABCD.

Predicate represents expressions that inform

some detail about the status of the world, such

as: (Is-patient-of (Patient :id 000000) (Clinic

:name "Dental Clinic ABCD")). It means that

the patient with the id 000000 is patient of the

clinic, which name is Dental Clinic ABCD.

We are particularly following the reference

model proposed by Fabio Bellifemine, Giovanni

Caire, and Dominic Greenwood in (Bellifemine et al

2007.) Figure 2 illustrates this model.

In this reference model we have the Predicate,

Concept, and AgentAction as Element. Moreover, the

Predicate is a ContentElement; the Concept is a

Term

; and the AgentAction is a specialization of

Concept. A ContentElement can be used as a content

of an ACL message. The Term can be an abstract

entity or a concrete entity.

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Figure 3: Ontological Java class (Interface Elements Standardization) to facilitate the Interface agent communication/inter-

operability with Dental Domain agents, heterogeneous devices, changeable contexts, and several Patients actors.

Figure 2: Content reference model (Bellifemine et al

2007).

In our dental case study, we have Elements in the

interface level; dental domain level, and dental

application level. We firstly defined an ontological

Java class that extends the Ontology class for each

interface Element in the dental context. Each

ontological Java class is declared as a singleton

object as this class is normally not evolved during

the agent’s lifetime. For the same reason, we defined

another Java class, which also extends the Ontology

class, and contains a static method in order to access

this singleton object. It means that different software

agents that are in the same Java Virtual Machine can

share the same ontology object.

An example of our ontological Java classes for

the dental case study in the interface level is

presented as a code fragment on Figure 3.

We are using the JADE-LEAP Platform

execution modes (split and standalone) (Caire 2003)

to deal with heterogeneous devices (MIDP and

Personal JAVA). Thus, the code fragment illustrates

our I/O MIDP ontology to support the

communication between the Interface agent –

located inside the MIDP device – and the Domain

agents. Only to clarify the idea, some interface

Elements – used by the Interface agent to

dynamically construct dental forms that will be

presented to the user using her/his own device and

according to the user’s preferences and the devices

features (e.g. memory/processing capacities, screen

size, and resolution) – are:

• SendMIDPForm agent action: is the ontological

representation for an action performed by the

Interface agent in order to send a form.

• MIDPStringItem concept: is an ontological

concept that describes a StringItem element,

which can be used to compose the Form, by

representing a spring.

• MIDPChoiceElement concept: is an ontological

concept that describes a ChoiceElement, which

can be used to compose the ChoiceGroup, by

representing the alternative text.

• MIDPChoiceGroup concept: is an ontological

concept that describes a ChoiceGroup, which

can be used to compose the Form, by

representing a group of choices. Moreover, it

can be composed of one or more

ChoiceElement(s).

• MIDPForm concept: is an ontological concept

that describes a Form, which can be composed

of zero or more StringItem(s), and zero or more

ChoiceGroup(s).

We can observe that the ontological Java class

basically contains the constructor and the structures

of the schema for different Concepts, AgentActions,

and Predicates. Each element in a schema has a

name and a type. An element can be declared as

"OPTIONAL" or "MANDATORY." An

"OPTIONAL" element means it can assume a "null"

...

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value. On the other hand, a "MANDATORY"

element means that an OntologyException will be

thrown if a "null" value was found. An element in a

schema can also be a list, in which, for example, the

cardinality of this element is zero or more String

type elements. The ontological Java class

implements the Vocabulary Java class, which code

fragment is illustrated on Figure 4.

Figure 4: Ontology vocabulary for Interface Elements.

Based on the FIPA Coder and Decoder classes

for SL Language, another important consideration is

that each schema must implement its proper

interface, as follows:

• for ConceptSchema, the class must implement

the Concept interface.

• for AgentActionSchema, the class must

implement the AgentAction interface.

• for PredicateSchema, the class must implement

the Predicate interface.

In order to exemplify this consideration, Figure 5

shows part of the code based on the MIDPForm

Concept.

As presented on (Bellifemine et al 2007), the

next three steps are necessary to conclude the

ontology: (i) define the content language; (ii)

a software agent; and (iii) create or manipulate the

content expressions as Java Objects.

The first step consists on defining the content

language. Using the FIPA Coder and Decoder we

have the possibility to choose the SL Language or

the LEAP language. It is also possible to develop an

agent that uses a proper language by implementing

the jade.content.lang.Codec interface. The SL

Language is a human-readable content language,

which content expression is a string.

Figure 5: MIDPForm Concept implements Concept.

The LEAP language is a non-human-readable

content language which content expression is a

sequence of bytes. Moreover, the LEAP language is

lighter than the SL language. This feature is

particularly interesting in strong memory and

processing limitations. In our dental case study we

used the SL language.

The second step consists on registering the

content language and the ontology using a software

agent. Normally, in behaviour-based agents, this

registration is performed in the agent setup() method

as presented on Figure 6 for the PatientInterface

agent – a JAVA code fragment. As this Interface

agent runs inside the MIDP device, we decided to

use a “light” agent, based on behaviour to avoid

problems with the device memory and processing

limitations.

Figure 6: Registering content language and ontology using

our behaviour-base PatientInterface agent.

However, as we are using intentional agents in

the domain level to improve the cognition capacity,

the “like me” recognition, and the goal formation,

we also registered the content language and the

ontology according to the JADEX specifications and

the BDI Notation as shown on Figure 7 – XML code

fragment of the DomainPatient agent (property tag.)

Figure 7: Registering content language and ontology using

our intentional-oriented DomainPatient agent.

...

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Figure 10: Creating/manipulating the content expressions using our DomainPatient agent (ExecuteRPRequestPlan).

The third step consists on creating and

manipulating the content expressions as Java

Objects. Figure 8 shows the code fragment about

this step using our PatientInterface agent.

Figure 8: Creating/manipulating the content expressions as

Java objects using our PatientInterface agent.

Again, in order to create and manipulate the

content expressions using intentional agents, we

extended the Plan class specified on the JADEX

documentation and we also implemented the

DecideRPRequestPlan and the

ExecuteRPRequestPlan as plans of our

DomainPatient agent. Figures 9 and 10 respectively

present code fragments of these plans.

3 LESSONS LEARNED

We have applied a FIPA-Standards-based

Figure 9: Creating/manipulating the content expressions

using our DomainPatient agent (DecideRPRequestPlan).

ontological support to Intentional-MAS-oriented

ubiquitous systems. Our experimental research

contributes to the systematic development of

Intentional-MAS-oriented ubiquitous systems as it

has shown that ontology improves the inter-

operability and the communication among

intentional autonomous entities in ubiquitous

environments. Among other contributions, our

efforts provide a reuse-based standard support to

specify the concepts, agent-actions and predicates

based on the cognitive domain of the ubiquitous

system-to-be.

As applied to our extensive dental case study,

and reported on this paper using a scenario based on

this case study, the proposed ontological support can

be extended to deal with Multiple-Multiplicity in

Ubiquitous Context (Tigli et al. 2009): many people

accessing many applications/services, using many

devices, communicating with many people located in

many places, considering many issues, and so on.

As our ontological support is centered on

intentional software agents, incremental updates on

their beliefs bases – including insertion, exclusion,

and modification operations – are simple to be

...

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performed. Moreover, FIPA-Standards-based

ontological support deals with privacy and access

control issues – intrinsic concerns in Ubiquitous

Computing - by contemplating the notion of a

personal agent into the agent’s communication

ontological model. Each final user – in our case

study, each patient – has a personal and cognitive

agent that represents her/him in the smart-spaces.

This agent is responsible for achieving the user’s

goals, based on her/his profile (e.g. privacy policies,

and preferences); and for controlling the user’s data

access by other users, agents, and organizations. If it

is desired by the final user, the access negotiation

process as well as the user’s knowledge sharing can

be “invisible” for the users. The process complexity

invisibility is a concern in Ubiquitous Computing –

idealized by Mark Weiser in his seminal paper

(Weiser 1991) – that we try to deal with using

intentional agents, standardize communication and

inter-operability protocols.

4 CONCLUSIONS

We presented our first efforts to improve the

systematic development of ubiquitous systems.

These efforts consist on an extensive experimental

research, in which we apply the FIPA standards

ontological support to the development of

Intentional MAS-oriented ubiquitous systems.

The work was performed in the Software

Engineering Laboratories at PUC-Rio and UofT,

where our Ubiquitous Computing group conducted

experiments by developing ubiquitous systems in

different cognitive domains (Serrano et al. 2008)

(Serrano et al. 2009) (Serrano and Lucena 2010).

In this paper, we particularly described the

ontology construction process – from the

investigation activities to the definition, registration,

creation and manipulation of the ontological model

using a dental scenario, Interface and Domain

agents, and Interface Elements. Moreover, we

presented the use of this ontological support in a

dental case study, which contemplated/addressed

some important concerns of ubiquitous

environments such as device heterogeneity, smart-

spaces distribution, services omnipresence, and

users’ satisfaction.

We also enriched our contributions by

incorporating this technological support in our

intentional agent-oriented approach – briefly

presented on (Serrano et al. 2009) – as a building

block composed of the ontology developed by our

group for Interface Level, Domain Level, and

Application Level, following the same construction

process presented in this paper with the I/O MIDP

Ontology code fragments. Our intention is to provide

a reuse-oriented support that aims the requirements

and software engineers teams in the multi-agent

communication standardization, knowledge

representation and sharing, and ubiquitous-issue-

based and context-aware interface dynamic

construction.

5 FURTHER WORK

We are combining our ontological support with a

dynamic database to appropriately store the

knowledge in Ubiquitous Systems (Serrano and

Lucena 2010). Among other contributions/benefits,

the dynamic structure can deal with constant

changes in ubiquitous scenarios – incorporation of

new technologies on devices, or changes on the

users’ preferences and intentions, or even

differences in the network features.

In this scenario, the knowledge can dynamically

be created, modified, deleted, shared, and reused by

multi-agents in a common agent-oriented platform

supported by the ontology briefly presented here.

As special policies are needed in order to

maintain the security and privacy (Campbell et al.

2002) (Kagal et al. 2004) of this knowledge, we are

also developing a layer structure mainly centered on

the concerns: security, privacy, and stakeholders’

satisfaction (Serrano and Lucena 2010). The

combination of this layer structure, the proposed

ontological set, and the dynamic database model

composes our building block for the Intentional

Systematic Software Development of Ubiquitous

Systems (ISSD for UbSystems.)

Furthermore, we are interested in experimental

work to evaluate the SOUPA (Standard Ontology for

Ubiquitous and Pervasive Applications) (Chen et al.

2004) in our ubiquitous projects, and to compare

SOUPA and FIPA standard ontology.

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