A PROCESS FOR MULTI-AGENT DOMAIN AND

APPLICATION ENGINEERING

The Domain Analysis and Application Requirements Engineering Phases

Adriana Leite

and Rosario Girardi

Department of Computer Science, Federal Institute of Maranhão, Avenida Getúlio Vargas, São Luís, Brazil

Department of Computer Science, Federal University of Maranhão, Avenida Dos Portugueses, São Luís, Brazil

Keywords: Software Process, Requirements Engineering, Multi-agent Systems, Reuse, Ontologies.

Abstract: Domain Engineering is a process for the development of a reusable application family in a particular

domain problem, and Application Engineering, the one for the construction of a specific application based

on the reuse of software artifacts in the application family previously produced in the Domain Engineering

process. MADAE-Pro is an ontology-driven process for multi-agent domain and application engineering

which promotes the construction and reuse of agent-oriented applications families. This article introduces an

overview of MADAE-Pro emphasizing the description of its domain analysis and application requirements

engineering phases and showing how software artifacts produced from the first are reused in the last one.

1 INTRODUCTION

A software development process is a model that

specifies a life cycle, describing the phases through

which transits a software product from its

conception through its development along with a

methodology that integrates the techniques to be

applied in each one of the phases according to a

development paradigm.

MADAE-Pro (“Multi-agent Domain and

Application Engineering Process”) is a process for

the development and reuse of families of multi-agent

software systems. A family of software systems is

defined as a set of systems sharing some

commonalities but also having particular features

(Czarnecki and Eisenecker, 2000). It consists of two

complementary sub-processes: Multi-agent Domain

Engineering and Multi-agent Application

Engineering.

Multi-agent Domain Engineering is a process for

the development of a family of multi-agent software

systems in a problem domain, by applying MADEM

(“Multi-agent Domain Engineering Methodology”)

(Girardi, 1992); and Multi-agent Application

Engineering, the one for constructing a specific

agent-oriented application by reusing one or more of

those families, using MAAEM (“Multi-agent

Application Engineering Methodology”) (Leite et

al., 2008). The MADAE-Pro products are

represented as facts of the ONTORMAS knowledge

base. ONTORMAS (“ONTOlogy driven tool for the

Reuse of Multi-Agent Systems”) (Leite et al., 2008)

is a knowledge-based tool for supporting and

automating the MADAE-Pro tasks.

The paper is organized as follows. Section 2

describes the MADAE-Pro software development

process. Section 2.1 introduces its lifecycle and a

general description of the support that the MADEM

and MAAEM methodologies provide to each one of

its phases. Section 2.2 details the particular tasks of

the Domain Analysis and Application Requirements

Engineering phases along with the guidelines

provided by these methodologies to carry out those

tasks. Section 3 references related work discussing

its similarities and differences with MADAE-Pro.

Finally, section 4 concludes the paper with some

considerations on ongoing work.

2 THE MADAE-Pro PROCESS

MADAE-Pro is a knowledge-based process. Its

phases, tasks and products are conceptualized in the

ONTORMAS knowledge-base and both, particular

or multi-agent system families are represented as

instances of this knowledge base.

Main modeling concepts and tasks of MADEM

and MAAEM are based both on techniques for

156

Leite A. and Girardi R. (2009).

A PROCESS FOR MULTI-AGENT DOMAIN AND APPLICATION ENGINEERING - The Domain Analysis and Application Requirements Engineering

Phases.

In Proceedings of the 11th International Conference on Enterprise Information Systems - Information Systems Analysis and Speciﬁcation, pages

156-161

DOI: 10.5220/0001990201560161

 SciTePress

Domain and Application Engineering (Czarnecki

and Eisenecker, 2000) and for development of multi-

agent systems (Bresciani et al., 2004) (Cossentino et

al., 2004) (Dileo,Jacobs and Deloach, 2002). A

specific graphical modeling language has been

defined for the representation of the MADAE-Pro

concepts. For the specification of the problem to be

solved, the methodologies focus on modeling goals,

roles and interactions of entities of an organization.

Entities have knowledge and use it to exhibit

autonomous behavior. An organization is composed

of entities with general and specific goals that

establish what the organization intends to reach.

The achievement of specific goals allows reaching

the general goal of the organization. Specific goals

are reached through the performance of

responsibilities that entities have by playing roles

with a certain degree of autonomy. Entities playing

roles have skills on one or a set of techniques that

support the execution of responsibilities in an

effective way. Pre-conditions and post-conditions

may need to be satisfied for/after the execution of a

responsibility. Knowledge can be consumed and

produced through the execution of a responsibility.

2.1 The MADAE-Pro Lifecycle

The MADAE-Pro process life cycle is iterative,

incremental and goal-driven. Development is carried

out through successive increments, looking for

reducing software complexity. It is initiated with the

decision of development of a new family of

applications or a specific one by specifying a new

general goal and restarted for the development of a

new specific goal or to update an existing one in

evolutive and corrective maintenance, respectively.

Iterations also can occur between the phases for

refining modeling products.

MADAE-Pro consists of six development

phases: domain analysis, domain design and domain

implementation, supported by the MADEM

methodology; and application requirements

engineering, application design and application

implementation, supported by the MAAEM

methodology.

The domain analysis phase of MADEM

approaches the construction of a domain model

specifying the current and future requirements of a

family of applications in a domain by considering

domain knowledge and development experiences

extracted from domain specialists and applications

already developed in the domain. This phase

consists of the following modeling tasks: modeling

of domain concepts, goal modeling, role modeling,

role interaction modeling and user interface

prototyping. The product of this phase, a domain

model, is obtained through the composition of the

products constructed through these tasks: a concept

model, a goal model, a role model, a set of role

interaction models, one for each specific goal in the

goal model and a prototype of the user interface.

Next section details the domain analysis tasks and

products.

The domain design phase of MADEM

approaches the architectural and detailed design of

multi-agent frameworks providing a solution to the

requirements of a family of multi-agent software

systems specified in a domain model. This phase

consists of two sub-phases: the architectural design

sub-phase which establishes an architectural model

of the multi-agent society including the knowledge

shared by all agents in their communication and

their coordination and cooperation mechanisms; and

the agent design sub-phase which defines the

internal design of each agent, by modeling its

structure and behavior. A Multi-agent Framework

Model of the Multi-agent Society is constructed as a

product of this phase, composed of a Multi-agent

Society Knowledge Model, an Architectural Model

and a set of Agent Models.

The domain implementation phase of MADEM

approaches the mapping of design models to agents,

behaviors and communication acts, concepts

involved in the JADE framework (Bellifemine et al.,

2003), which is the adopted implementation

platform. An implementation model of the multi-

agent society is constructed as a product of this

phase, composed of a model of agents and behaviors

and a model of communication acts.

Variability modeling is carried out in parallel

with all MADEM phases to determine the common

and variable parts of an application family. This is

done by identifying the “Variation Points” and its

correspondent “Variants”. A variation point is the

representation of a concept subjected to variation. A

variant represents the alternative or optional

variations of such a concept.

MAAEM is a methodology for requirement

analysis, design and implementation of multi-agent

applications through compositional reuse of software

artifacts such as domain models, multi-agent

frameworks, pattern systems and software agents

previously developed in the MADEM Domain

Engineering process.

The requirements analysis phase of MAAEM

looks for identifying and specifying the

requirements of a particular application by reusing

requirements already specified in domain models.

This phase follows a set of modeling tasks

consistently uniform with the ones of the MADEM

A PROCESS FOR MULTI-AGENT DOMAIN AND APPLICATION ENGINEERING - The Domain Analysis and

Application Requirements Engineering Phases

157

domain analysis phase, for producing a set of models

composing the multi-agent application specification.

The MAAEM requirements analysis phase is

performed through the following modeling tasks:

concept modeling, goal modeling, role modeling,

role interaction modeling and user interface

prototyping. The product of this phase, an

application specification, is obtained through the

composition of the products constructed through

these tasks: a concept model, a goal model, a role

model, a set of role interaction models, one for each

specific goal in the goal model and a prototype of

the user interface. Next section details the

requirements analysis tasks and products.

In the application design phase, developers reuse

design solutions of a family of applications and

adapt them to the specific requirements of the

application under development. A set of models

composing the multi-agent application architecture

are produced by following a set of modeling tasks

consistently uniform with the ones of the MADEM

domain design phase. This phase consists of two

tasks: the Architectural Design task aiming at

constructing a multi-agent society architectural

model and the Agent Design task, which defines the

internal structure of each agent in the society. The

Architectural Design task consists of five sub-tasks:

Multi-agent Society Knowledge Modeling, Multi-

Agent Society Modeling, Agent Interaction

Modeling, Activity Modeling, and Coordination and

Cooperation modeling.

In the application implementation phase, agent

behaviours and interactions are identified and

specified in a particular language/platform for agent

development. A Behaviors Model and

Communication Acts Model are generated in this

development phase.

Along all MAAEM phases, reuse is carried out

by identifying variation points in MADEM products

and selecting appropriate variants.

2.2 The Domain Analysis and

Application Requirements

Engineering Tasks

This section describes the Domain Analysis and

Application Requirements Engineering tasks of

MADAE-Pro showing how software artifacts of the

ONTOSERS domain model (Mariano et al., 2008)

are produced and reused on the development of the

InfoTrib multi-agent recommender system.

ONTOSERS-DM is a domain model that specifies

the common and variable requirements of

recommender systems based on the ontology

technology of the Semantic Web, using three

information filtering approaches: content-based

(CBF), collaborative (CF) and hybrid filtering (HF)

(Mariano et al., 2008).

InfoTrib is a Tax Law recommender system in

which, based on a user profile specifying his/her

interests in the diverse species of taxes, the system

provides recommendations based on new tax law

information items.

The Domain Analysis Tasks. The modeling of

domain application concepts task aims at performing

a brainstorming of domain concepts and their

relationships, representing them in a concept model.

The purpose of the goal modeling task is to

identify the common and variant goals of the family

of systems, the external entities with which it

cooperates and the responsibilities needed to achieve

them. Its product is a goal model, specifying the

general and specific goals of the system family

along with the external entities, responsibilities and

variant groups. In this task, variability modeling

looks for identifying variant points in specific goals

related with variant groups of responsibilities.

The goal model of ONTOSERS-DM has the

“Provide Recommendations using Semantic Web

Technology” general goal. It is reached through the

“Model Users”, “Filter Information” and “Deliver

Recommendations” specific goals. In order to

achieve the “Filter Information” specific goal, it is

necessary to perform the “Ontology Instance User

Model Creation and Update” responsibility, which

also contributes to reach the “Model Users” specific

goal. Besides that, the “Grouping of user models”,

“Information Items based on Ontology Instance

Representation” and “Similarity Analysis”

responsibilities are needed. The “Grouping of Users

Models” responsibility allows for identifying groups

of users with similar interests.

The “Model Users” specific goal has a variation

point with groups of responsibilities for user profile

acquisition, being possible to choose between three

alternative variants: “Implicit Profile Acquisition”,

“Explicit Profile Acquisition” or both. The last

responsibility, “Ontology Instance User Model

Creation and Update” is fixed, i.e. it is required in all

the applications of the family. The “Filter

Information” specific goal has a variation point that

has as variant alternatives: the “Grouping of users

models” responsibility, required in systems that use

CF; and the “Information Items based on Ontology

Instance Representation” responsibility required in

the ones using CBF. The “Deliver

Recommendations” specific goal does not have

variation points, therefore the “Similarity Analysis”,

“Personalized Recommendations Production” and

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“Delivery of Personalized Recommendations”

responsibilities are required in all the applications of

the family, then belonging to the fixed part of the

goal model.

The role modeling task associates the

responsibilities, either common or variants,

identified in the goal modeling task to the roles that

will be in charge of them. The pre and post-

conditions that must be satisfied before and after the

execution of a responsibility are also identified.

Finally, the knowledge required from other entities

(roles or external entities) for the execution of

responsibilities and the knowledge produced from

their execution is identified. This task produces a set

of role models, one for each specific goal or, having

it one or more variation points, one role model for

each variant, specifying roles, responsibilities, pre-

and post-conditions, knowledge and relationships

between these concepts. The role interaction

modeling task aims at identifying how external and

internal entities should cooperate to achieve a

specific goal. For that, responsibilities of roles are

analyzed along with their required and produced

knowledge specified in a role model. A set of role

interaction models is constructed as a product of this

modeling task, one for each specific goal, or having

it one or more variation points, one role interaction

model for each variant, specifying the interactions

between roles and external entities needed to achieve

a specific goal. The interactions are numbered

according to their sequencing. Finally, a User

Interface Prototype, is developed by identifying the

interactions of users with the system family.

The Application Requirements Engineering

Tasks. In this phase, the reuse of domain models is

supported by the ONTORMAS tool. In

ONTORMAS, the selection of software artifacts is

supported by semantic retrieval, where the user

inputs a query specifying the product features he/she

intends to reuse and gets as result the available

artifacts from the repository which satisfies his

query. After the selection of the artifact that most

closely matches their needs, the user should check if

the artifact can be integrally reused or if it needs

adaptations and/or integrations with other artifacts.

The modeling of application concepts task aims

at performing a brainstorming of the application

concepts and their relationships, representing them

in a concept model. The purpose of the goal

modeling task is to identify the goals of the

application, the external entities with which it

cooperates and the responsibilities needed to achieve

them. Its product is a goal model, specifying the

general and specific goals of the application along

with the external entities and responsibilities. This

task should be reuse-intensive. From the concept

model and from a first draft of the goal model,

possible terms for searching and reusing goals in

already available domain models can be revealed. If

a general goal is identified, the corresponding goal

model in a domain model is selected for reuse. If a

specific goal is identified, this goal, sub-goals in a

possible hierarchy, related responsibilities and

external entities in a goal model of a domain model

are selected for reuse. Otherwise, the goal model is

constructed from scratch.

If a selected specific goal or sub-goals in its

hierarchy have associated variation points, they

should be analyzed to select and possible reuse the

appropriate variants of alternative or optional groups

of responsibilities. Only one group of

responsibilities in an alternative variant can be

selected for reuse. Zero or more groups of

responsibilities in an optional variant can be selected

for reuse.

To construct the goal model of InfoTrib, first, a

search in ONTORMAS with the term

“recommendation” was done. The general goal

“Provide recommendations using semantic web

technologies” was retrieved through the search.

Therefore, the corresponding goal model was

selected for reuse, in this case, the goal model of

ONTOSERS, part of the ONTOSERS domain

model. From the variation point of the “Model

users” specific goal, the ”Explicit profile

acquisition” responsibility variant was selected in

order to support just the explicit acquisition of user

profiles. From the variation point of the “Filter

Information” specific goal, the “Information Items

based on Ontology Instance Representation”

responsibility variant was selected for providing

content-based information filtering. The names of

the external entities “User” and “Ontology based

information source” were specialized to “Tributary

User” and “ONTOTRIB”, the ontology that defines

the Tax Law concepts and relationships.The role

modeling task associates the responsibilities

identified in the goal modeling task to the roles that

will be in charge of them. The pre and post-

conditions that must be satisfied before and after the

execution of a responsibility are also identified.

Finally, the knowledge required from other entities

(roles or external entities) for the execution of

responsibilities and the knowledge produced from

their execution is identified. A set of role models,

one for each specific goal in the goal model is

constructed in this task, with or without reuse.The

following rules apply for the reuse activities

performed during this modeling task:

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• If a similar general goal was identified during

the goal modeling task, thus reusing fully or partially

a goal model then, the set of role models, already

available in the corresponding domain model and

associated to each reused specific goal will be

reused and eventually adapted for the previously

customized specific goals and selected

responsibilities from groups of alternative or

optional variants.

• Otherwise, if a set of similar specific goals

were identified during the goal modeling task, thus

reusing partially a goal model, then the set of role

models already available in the corresponding

domain model associated with the similar specific

goal will be reused and eventually adapted,

considering selected responsibilities from groups of

alternative or optional variants.

• Otherwise, if the goal model was constructed

from scratch, then the set of role models will be

also constructed from scratch, one for each specific

goal. Please note that in this task, reuse is implicitly

supported by the semantic relationships, that

associates a specific goal with a role model.

The role interaction modeling task aims at

identifying how external and internal entities should

cooperate to achieve a specific goal. For that,

responsibilities of roles are analyzed along with their

required and produced knowledge specified in a role

model. A set of role interaction models is reused in

this modeling task, one for each specific goal, or

having it one or more variation points, one role

interaction model for each variant, specifying the

interactions between roles and external entities

needed to achieve a specific goal. The interactions

are numbered according to their sequencing. Similar

rules to the ones of the role modeling task apply for

the reuse activities performed during this modeling

task. For the construction of the user interface

prototype of the specific application,the generic

interfaces associated to a reused external entity are

selected and customized according the specific goal

with which it is related.

3 RELATED WORK

Some approaches for agent development, like GAIA

(Zambonelli et al., 2003), PASSI (Cossentino et al.,

2004) and TROPOS (Bresciani et al., 2004), have

been already developed to increase the productivity

of the software development process, the reusability

of generated products, and the effectiveness of

project management.

GAIA is a methodology based in human

organization concepts. It supports the analysis and

design phases for multi-agent system development.

Tropos is an agent-oriented software development

methodology supporting the complete multi-agent

development process. It is based on the i*

organizational modeling framework. PASSI (a

Process for Agent Societies Specification and

Implementation) is a process for multi-agent

development integrating concepts from object-

oriented software engineering and artificial

intelligence approaches. It allows the development

of multi-agents systems for special purposes as

mobiles and robotics agents and uses an UML-based

notation.

Some characteristics of GAIA, PASSI, TROPOS

and MADAE-Pro are following described. All the

approaches propose an iterative life cycle, where a

software product goes through several refinements

during the development process. With the exception

of GAIA, in all other approaches the life cycle is

also incremental, where a software product is

represented in several models to facilitate its

understanding.

For the supported development phases, all these

approaches cover analysis and design while PASSI,

TROPOS and MADAE-Pro also support the

implementation phase. To our knowledge, only

MADAE-Pro allows the development of families of

systems. For the available development tools, PASSI

is supported by PTK, a Rational Rose plug-in

allowing modeling in AUML and code generation.

The application of TROPOS is assisted by the

TAOM-Tool (Bresciani et al., 2004), an Eclipse

plug-in allowing system modeling with the i*

framework. The MADAE-Pro process is supported

by the ONTORMAS tool that allows the modeling

and storage of individual applications and families

of multi-agent applications as instances of

ontologies. GAIA does not have a tool support yet.

Finally, for reuse activities, GAIA and TROPOS

allow the reuse of models and code in an informal

way. PASSI permits the reuse of source code from

class and activity diagrams. MADAE-Pro process

allows reuse of both models and source code of

software products.

Two main features distinguish MADAE-Pro

from other existing approaches. First, it provides

support for reuse in multi-agent software

development, through the integration of the concepts

of Domain Engineering and Application

Engineering. Second, it is a knowledge-based

process where models of agents and frameworks are

represented as instances of the ONTORMAS

knowledge base. Thus, concepts are semantically

related allowing effective searches and inferences

thus facilitating the understanding and reuse of the

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models during the development of specific

applications in a domain. Also, the ontology-driven

models of MADAE-Pro can be easily documented,

adapted and integrated.

4 CONCLUSIONS AND

FURTHER WORK

This work described MADAE-Pro, a process for

Multi-agent Domain and Application Engineering,

emphasizing the description of its domain analysis

and application requirements engineering phases and

showing how software artifacts produced on the first

phase are reused in the last one.

The software artifacts produced by MADAE-Pro

are represented as instances of the ONTORMAS

tool, which serves as a repository of the MADAE-

Pro reusable software artifacts and is a knowledge-

based tool supporting application development.

MADAE-Pro has been evaluated with several

case studies approaching both the development of

application families (Mariano, Girardi, Leite,

Drumond and Maranhão, 2008) and specific

applications (Newton and Girardi, 2007). It makes

part of a project for the improvement of multi-agent

development techniques, methodologies and tools.

With the knowledge base provided by

ONTORMAS, an expert system is being developed,

aiming at automating various tasks of both MADEM

and MAAEM, thus allowing fast application

development and partial code generation.

MADAE-Pro currently supports compositional

reuse, based on the selection, adaptation and

composition of software artifacts. A generative

approach for reuse has been already explored with

the specification of the GENMADEM methodology

and the ONTOGENMADEM tool (Jansen and

Girardi, 2006). ONTOGENMADEM provides

support for the creation of Domain Specific

Languages to be used on the generation of a family

of applications in a domain. Further work will

extend ONTORMAS for supporting

ONTOGENMADEM allowing generative reuse in

Multi-agent Applications Engineering.

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