MAS Ontology: Ontology for Multiagent Systems

Felipe Cordeiro

, Vera Maria B. Werneck

, Neide dos Santos

and Luiz Marcio Cysneiros

Universidade do Estado do Rio de Janeiro, Master Program in Computational Science, Rio de Janeiro, Brazil

York University, School of Information Technology, Toronto, Canada

Keywords: Agent-based Software Engin

eering, Domain Ontology, Comparison of Agent-oriented Methodologies.

Abstract: This work describes the Multiagent Systems (MAS) Ontology to assist in the development of multi-agent

system using different methodologies. The MAS Ontology consists of fragmenting agent-oriented

methodologies following an ontology approach based on the best aspects of four prominent AOSE

methodologies and Guardian Angel exemplar that identify the strengths, weaknesses, commonalities and

differences. In this paper, we present a brief explanation of Multiagent methodologies and the step-by-step

process to describe the agent-based systems domain and how it can be represented. Given the numerous works

in the literature about MAS methodologies, our aim is to help select the best and more appropriate properties

to be used in Multiagent Systems development.

1 INTRODUCTION

In the past two decades, the agent technology

approach has been considered as a new paradigm for

developing complex systems. This approach has

attracted an increasing amount of interest from the

research community and has demonstrated its

potential in many fields, such as: (i) working with

different types of distributed devices (e.g., sensor

networks, mobile phones, and personal computers),

(ii) enabling various types of communication and data

exchange (e.g., audio and video), and (iii) ability to

dynamically adapt to the ever changing requirements

and dynamic operating environment (Munroe et al.,

2006), (Pěchouček and Mařík, 2008), (Dam and

Winikoff, 2013).

Agent-oriented systems must be built in terms of

autonomous task-oriented entities. They need to be

organized to interact (cooperate, coordinate and

negotiate) with one another. To adopt the agents’

perspective requires a new set of tools to support

software development (Cernuzzi, Cossentino and

Zambonelli, 2005).

Currently, we are faced with a multitude of

different frameworks, some of them even supported

by tools. However, very few methodologies are broad

enough to support the whole software development

life cycle or to support the complexity of developing

such systems. Years ago, Luck, Mcburney and Preist

(2003) stated: "One of the most fundamental

obstacles to large-scale take-up of agent technology

is the lack of mature software development

methodologies for agent-based system".

In this work, we assume "methodology" as a set

of phases that a practitioner must go through to design

an agent-based system. We see a methodology as

being composed of general concepts (deals with the

question of whether a methodology adheres to the

basic notions of agents and multiagent systems),

specific concepts (underlying one particular

capability or a characteristic), notation (symbols used

to represent elements), modeling techniques (set of

models that depict a system at different levels of

abstraction and different aspects of the system),

process (development aspect) and pragmatics

(practical implementation aspects) (Sturm and

Shehory, 2004).

The main goal of this paper is to provide an

ontology structure for selecting the best and more

appropriate artefacts to be used to develop one

particular Multiagent Systems, the MAS Ontology. It

is motivated by a large number of existing approaches

and supported by our experience in using some of

them. It is important to notice that this work does not

claim nor intends to be complete. It is expected to be

a first approach that will be perfected overtime but

that will yet be of importance to help developers to

use the best each of the current four (Gaia, MaSE,

Prometheus and Tropos) methodologies covered in

this work has to offer.

The main goal of the ontology proposed in this

work is to capture and facilitate the reuse of

536

Cordeiro, F., Werneck, V., Santos, N. and Cysneiros, L.

MAS Ontology: Ontology for Multiagent Systems.

In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) - Volume 1, pages 536-543

ISBN: 978-989-758-187-8

knowledge gained through the evaluation of several

MAS methodologies based on more than 20 projects

developed with different methodologies using

Guardian Angel (GA) Exemplar proposed by Yu and

Cysneiros. However, to complement the ontology we

added experiences extracted from other well know

evaluation studies from the literature. We populated

the ontology with the four methodologies because

they were evaluated by GA, Sturm and Shehory

(2004, 2014) and Dam and Winikoff (2004, 2013).

2 AGENT-ORIENTED

METHODOLOGIES

Cernuzzi et.al. (2005) suggests a clean and

disciplined approach to analyzing, designing and

developing multiagent systems, using specific

methodologies and techniques by means of notations,

diagrams and tools to support the development.

We assume that each method has strengths and

weakness, and these characteristics may influence the

use of one methodology over another for one specific

project. To validate the MAS Ontology we used four

methodologies, namely, Gaia (Zambonelli, Jennings

and Wooldridge, 2003), (Wooldridge, Jennings and

Kinny, 2000), MaSE (Deloach, 2001), (Deloach,

2004), Prometheus (Padgham and Winikoff, 2002),

(Padgham and Winikoff, 2003) and Tropos (Bresciani

et al, 2004).

Jennings and Wooldridge proposed Gaia in 1999.

It was extended and modified by Zambonelli in 2000

(Wooldridge, Jennings and Kinny, 2000), finally

Zambonelli, Jennings and Wooldridge presented a

stable version in 2003 (Zambonelli, Jennings and

Wooldridge, 2003). Unlike many other

methodologies, Gaia starts from modelling

requirements. Later it guides developers to a well-

defined design for the multiagent system, that way

programmers can easily model and implement it,

while dealing with the characteristics of complex and

open multiagent systems.

MaSE methodology is heavily based on UML and

RUP. It is divided into seven phases: capturing goals,

applying use cases, refining roles, creating agent

classes, constructing conversations, assembling agent

classes and system design (Deloach, 2001), (Deloach,

2004).

Prometheus is an iterative methodology covering

the complete software engineering process while

aiming at the development of intelligent agents (in

particular BDI agents). The concepts applied are

goals, beliefs, plans, and events, resulting in a

specification that can be implemented with JACK

(Coburn, 2000). Prometheus covers three phases: the

system specification, architectural design phase,

detailed design phase (Padgham and Winikoff, 2002),

(Padgham and Winikoff, 2003).

Tropos relies on the notion that an agent is based

on goals and tasks adopted by the i* framework (Yu,

2009) and offers supports to applications, particularly

for the development of BDI agents and the agent

platform JACK. (Coburn, 2000). Tropos consists of

four phases: early requirements, late requirements,

architecture design, detailed design and

implementation (Bresciani et al, 2004), (Tropos,

2014), (Coburn, 2000).

3 EVALUATION OF AGENT

ORIENTED METHODOLOGIES

Several evaluations of agent orientated methodologies

have been published (Dam and Winikoff, 2014),

(Sturm and Shehory, 2014), (Dam, 2003), (Dam and

Winikoff, 2004), (Tran and Low, 2005), (Elamy and

Far, 2008), (Iglesias, Garijo and González, 1999),

(Cernuzzi, Rossi and Plata, 2002), (Sure, Staab and

Studer, 2002). Sturm and Shehory (2004, 2014), and

Dam and Winikoff (2004, 2013), (Dam, 2003) were the

most cited works in the MAS area.

Sturm and Shehory (2004), proposed a

framework for quantitative and qualitative evaluation

of MAS methodologies (Gaia, MaSE and Tropos). It

explores the following aspects: concepts, properties,

notations and modeling techniques, process and

pragmatics. Dam and Winikoff (2004, 2013), (Dam,

2003) illustrate the strengths and weaknesses of

MaSE, Prometheus and Tropos methodologies

through an attribute-based evaluation process.

The Guardian Angel (GA) Exemplar proposed by

Yu and Cysneiros (Yu and Cysneiros, 2002) defines

a set of questions to evaluate the behaviour of MAS

methodologies and is expressed in terms of a set of

numbered scenarios. The GA is an easily

comprehended open system that provides automated

support to assess patients with chronic diseases

through a set of “guardian angel” software agents.

The GA exemplar is a complete solution, with a

practical, real and significant enough example, to test

and verify how the methodology behaves in close-to-

real situations. The primary concern of the exemplar

is to highlight the strengths, weaknesses and

potentials of each methodology justified by the

artefacts (work products) that can be used to answer

the methodology questions.

MAS Ontology: Ontology for Multiagent Systems

537

We chose to use the GA exemplar as it was the

only one we found in the literature that proposes

complex situations that can be used empirically to

evaluate different methodologies that go beyond toy

problems.

4 DOMAIN THEORY:

AGENT-ORIENTED

METHODOLOGIES

In order to define a Domain Theory for Agent-

Oriented Methodologies, we have compiled the

knowledge gathered from papers on AOSE

methodologies listed in section 2 (Dam and Winikoff,

2013), (Luck, Mcburney and Preist, 2003), (Sturm

and Shehory, 2014), (Tran and Low, 2005), (Elamy

and Far, 2008) together with the results from our

experience using the Guardian Angel exemplar over

the past 10 years.

While building the MAS Ontology, we tried to

answer the following research questions: (i) in what

situations is a methodology or method fragment best

applied?; (ii) which instruments are used to define the

methodological questions from GA and from the

works of (Dam and Winikoff, 2013), (Luck,

McBurney and Preist, 2003), (Sturm and Shehory,

2014), (Tran and Low, 2005), (Elamy and Far, 2008)

(iii) what are the general concepts of agents that a

MAS methodology should support?; (iv) what are the

specific concepts of agents that a MAS methodology

can support?; (v) what are the notations and modeling

techniques found in the methodology?; (vi) what are

the support resources offered by the methodology?

4.1 Approach

In the ontology, we assembled the knowledge

generated by using the GA exemplar pertinent to four

different methodologies (Gaia, MaSE, Prometheus

and Tropos). We organized the knowledge and

experiences gained by signaling which work product

is responsible for a certain task when answering the

questions listed above while applying the exemplar to

each of the aforementioned methodologies.

4.2 MethodBase GA: Experience

Modeling with GA

The MethodBase GA is the knowledge base that

compiles the work done over many years by MSc and

last year undergrad Computer Science students.

During this time, these students modeled multiagent

systems using methodologies such as Gaia, MaSE,

Prometheus and Tropos and relying on scenarios

proposed in Guardian Angel exemplar. After

modeling the solutions, the students answered the

methodological issues in accordance with strengths,

neutral or weakness, as seen in figure 1.

Figure 1: Methodbase GA.

4.3 Evaluated MethodBase: Evaluating

Methodologies

The Evaluated MethodBase is the knowledge base

built based on the work of Sturm and Dam (Sturm and

Shehory, 2014), (Dam and Winikoff, 2013), as

illustrated in figure 2. The proposed Evaluated

MethodBase includes the following concepts:

General Concepts of MAS, Specific Concepts of

MAS, Notations, Modeling Techniques, Process and

Pragmatics (practical aspects).

The ranking of values ranges from 0 to 6, where 0

represents cases where a certain characteristic is not

applicable, 1

Refers to but not detailed, 2 Limited, 3 Neutral, 4

Small issues, 5 Minor deficiencies

and 6 is the ideal

efficiency. This was an adaptation of (Sturm and

Shehory, 2004), (Dam and Winikoff, 2002) using the

databases Methodbased GA and Evaluated

Methodbase.

5 ONTOLOGY LEARNING FROM

EXPERIENCE

Many definitions of ontology can be found in the

literature. However, Sure (Sure, Staab and Studer,

2002) provides a simple and comprehensive

definition: "An ontology is a formal and explicit

specification of a shared conceptualization". In this

definition "formal" means readable by computers;

"explicit specification" refers to concepts, properties,

relations, functions, constraints, axioms, explicitly

defined; "shared" means consensual knowledge, and

"conceptualization" refers to an abstract model of

ICEIS 2016 - 18th International Conference on Enterprise Information Systems

538

some phenomenon in the world real. The ontology

built in this work was based on a middle-out strategy

(Uschold and King, 1995), in which concepts were

generalized and specialized.

Figure 2: Evaluated Methodbase.

Building on identify concepts (terms) that can

provide short assertive sentences, we developed an

ontology based on the knowledge acquired from both

databases Methodbased GA and Evaluated

Methodbase.

The ontology development is defined through

seven stages: Ontology Specification, Knowledge

Acquisition, Conceptualization, Formalization,

Integration, Implementation and Evaluation.

The Ontology Specification is used to prepare a

document using natural language, containing

information such as the primary ontology goal and its

other purposes.

Knowledge Acquisition focuses on possible

sources of knowledge. In this survey the GA

experiences were used in order to manage the data

collected, analyzed and categorized according to their

degree of strength, weakness or neutrality.

Conceptualization focuses on structuring the

domain knowledge into a conceptual model and was

based on the acquired vocabulary in the previous

phases, in order to describe the problems and their

possible solutions

In the Formalization, the concepts are now

formally written through OWL. The Protégé tool

version 4.3 (Protege, 2000) was used, and the first

preliminary version of the ontology was generated. At

this stage, a taxonomy that shows the processes of a

multiagent system is available.

The Integration stage obtains the representative

experimental ontology from the Guardian Angel

exemplar and is re-evaluated to better address the

domain of multiagent systems.

At this stage, other studies on the comparison of

methodologies are integrated. (Sturm and Shehory,

2004).

The Implementation used the Pellet, a Protégé

plugin to automatically check the ontology

consistency and also takes into account the

experience of validating the data, as well as

establishing the comparable relationship between the

Methodbase GA and Evaluated Methodbase values,

classes and attributes. Each phase of the ontology is

related to models, tables or charts, which serve to

guide the building process of the MAS Ontology, here

defined as products, as seen in figure 3.

Figure 3: MAS Ontology.

MAS Ontology: Ontology for Multiagent Systems

539

6 MAS ONTOLOGY AND

RESULTS

The MAS ontology has three main classes:

Methodology, Phases and Work_Products.

The class Methodology focuses on the

methodologies that are the study objects (Tropos,

ADELFE, Gaia, Prometheus, MaSE, and

MESSAGE).

The Phases class combines the characteristics

essential to multiagent systems (General Concepts,

Modeling Techniques, Notation, Pragmatics, Process

and Specific Concepts). Each class has a set of

attributes associated with it. (e.g. General Concepts

attributes such as: Autonomy, Reactiveness,

Sociality, Proactiveness, Reasoning, Mobility).

The class Work_Products lists the necessary

artifacts to build a multiagent system.

Figure 3 shows a simplified MAS ontology. The

Phase class is associated with the Methodology class.

In this relationship, subclasses of Phase are related to

subclasses of Methodology. The class

Work_Products is also listed to illustrate the artefacts

in Figure 4. It is important to determine which

attributes from the Phases class might be associated

with corresponding work products. For example, in

Figure 5 the subclass Tropos_Products has two

phases: Tropos_Analysis and Tropos_Design. Each

subclass has its own subclasses. Tropos analysis is

composed of Actors Diagram and Reasoning

Diagram. Tropos Design consist of Extended Actors

Diagram, Table of Actors and Capabilities, Table of

Agents, Agents Interaction Diagram, Tasks or Plans

Diagram and Capabilities Diagram.

6.1 Schematic Model

In order to facilitate understanding the domain of

multiagent systems by ontological representation, a

Schematic model of MAS Ontology (Figure 6) was

developed to illustrate the relationship between

classes, attributes and expected values.

In Figure 6 the schematic forms are described as

follows:

• Ellipse Form - Classes or Subclasses

• Rectangle Form - Attributes

• Dotted Ellipse - Value types for attributes

• Dotted Rectangle Form - Class properties

Figure 4: Work Products Detailed.

Figure 5: Tropos Work Products.

ICEIS 2016 - 18th International Conference on Enterprise Information Systems

540

Figure 6: Schematic model of MAS Ontology.

6.2 Examples

For MAS Ontology population the individuals were

separated into two groups: (i) representing specific

values of GA attributes (Yu and Cysneiros, 2002)

and (ii) representing the set of attributes that make

up a methodology in the evaluation comparison

papers (Sturm and Shehory, 2004, 2014), (Dam and

Winikoff, 2002, 2013). Thereby the query may

return a particular specific situation or a

methodology.

Figure 7a represents an unsuccessful search

carried out in plugin DL Query (Protege, 2000),

where the attribute Mobility (The quality or state of

being mobile) was defined as value 4, and no

individual was found. Figure 8b represents a

successful search done in plugin DL Query, where

the attribute Mobility was defined as value 3

In this scenario, three methodologies were

found (figure 7b). Figure 8 shows the associated

work products (e.g. Mobility Tropos and Mobility

Prometheus) obtained from a refined search for

Tropos and Prometheus.

Figure 7.a: Unsuccessful Search.

Figure 7.b: Successful Search.

MAS Ontology: Ontology for Multiagent Systems

541

Figure 8: Associated Work Products.

7 CORRELATED WORKS

Several works addressing the evaluation of agent

orientation methodologies have been published

(Dam and Winikoff, 2002), (Sturm and Shehory,

2004), (Dam, 2003), (Dam and Winikoff, 2004),

(Tran and Low, 2005), (Elamy and Far, 2008),

(Iglesias, Garijo and González, 1999), (Cernuzzi,

Rossi and Plata, 2002), (Sure, Staab and Studer,

2002), (Casare et al, 2014). They consist of

quantitative and qualitative evaluation framework

based on checklists of certain properties, qualities,

attributes or characteristics of the methodology and

some simple problems.

Tran and Low (2005, 2005a) compared ten

methodologies (Gaia, Tropos MAS

CommonKADS, Prometheus, Passi, ADELFE,

MaSE, RAP, MESSAGE, Ingenius). They used a

criteria checklist that was developed to assess the

resources of the chosen methodologies, covering the

process, techniques and model stages.

Cernuzzi and Zambonelli (2011) used the

multivalued statistical method for quantitative

evaluation of profiles. The goal was to present the

potential profile analysis in the comparison process

for the evaluation of methodologies, searching for

similar evaluations to confirm the results.

Our study differs from similar works by

proposing the use of a knowledge base where the

knowledge is expressed and organized as an

ontology. The ontology can guide the developer to

select fragments of methodologies that best fit the

multiagent system under development. It allows for

queries to be made that can help developers to

customize their development process. It helps them

to search for where the methodologies best fit their

needs considering the specific project at hand.

On another level, it also helps researchers

further developing these methodologies to easily

compare where their approaches fall behind when

compared to other existing methodologies and

therefore, where they should invest more effort to

develop furthere their methodologies.

8 CONCLUSIONS

As a result of the fast dissemination of MAS

methodologies, deciding what methodology to use

in a project is a complex task. Many frameworks

and toolkits are provided, but they do not always

offer support to assist developers in choosing the

best or most appropriate methodology to handle the

project at hand. This paper proposes an ontology-

based support to help developers faced with the

need to use agent-oriented properties to develop

software. The ontology was created based on the

experience gathered by applying the Guardian

Angel exemplar in four agent-oriented software

engineering methodologies, as well as adding the

knowledge obtained from the results from Sturn and

Dam (2004) and Dam (2003). The knowledge base

provided in this ontology can assist developers to

use these methodologies and also to choose better

the adequate artifacts for a particular domain.

The MAS Ontology approach focuses on being

a facilitator for developing a MAS process, as it

concentrates on relationships between the principles

of software engineering evaluation and experience.

Furthermore, it can be extended to suit the

particularities of other AOSE methodologies and

other studies based on statistics, as in (Iglesias,

Garijo and González, 1999).

Future works will address a systematic

validation of the Ontology using case studies where

different groups of randomly selected students will

be asked to develop solutions to a specific problem.

Some students will use the ontology, and another set

of students will have to develop the solution using

pre-determined methodology. Final results will

then be compared.

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