MGAIA: EXTENDING THE GAIA METHODOLOGY TO MODEL

MOBILE AGENT SYSTEMS

Weanna Sutandiyo, Mohan Baruwal Chhetri, Seng Wai Loke and Shonali Krishnaswamy

School of Computer Science and Software Engineering, Monash University, 900 Dandenong Rd,

Caulfield East, VIC 3145, Australia

Keywords: Mobile Agent Systems, Agent Oriented Software Engineering

Abstract: Mobile agents are a class of software agents that have the ability to move from host to host and are

particular

ly relevant for mobile and distributed applications. The development of several mobile agent

implementation environments has necessitated conceptual modelling techniques for mobile agent

applications. In this paper, we present mGaia, our extension of the Gaia Agent Oriented Software

Engineering (AOSE) methodology to model mobile agent systems.

1 INTRODUCTION

Mobile agents are a class of software agents that

have the ability to move and are particularly useful

in the area of distributed and mobile computing

(Kotz, 2002). Even though a sizable number of

mobile agent toolkits have been developed to

support the implementation and deployment of

mobile multiagent applications, the conceptual

modelling of mobile agent applications is an area

that has not been largely addressed (Krishnaswamy,

2003). The increasing focus on mobile agents as an

important technology for developing mobile and

distributed applications and the inherent

disadvantages of confining the mobility of agents to

implementation alone necessitates techniques for

modelling such systems prior to embarking on the

implementation. Agent Oriented Software

Engineering (AOSE) is defined as software

engineering for agent based computing (Weiß,

2002). AOSE methodologies aim to provide tools

and techniques for modelling, analysing and

designing agent systems prior to implementation.

Several methodologies have been proposed to model

multiagent systems (MAS). However, the focus of

these methodologies has been on multiagent systems

at a generic level and they do not address the

specific modelling issues that pertain to mobile

agent systems. We have developed a conceptual

modelling methodology for mobile agent systems,

mGaia, which is an extension of the Gaia Agent

Oriented Software Engineering (AOSE)

methodology (Woolridge, 2000). The paper is

organised as follows. Section 2 presents mGaia.

Section 3 presents an application that was modelled

with mGaia and then implemented using

Grasshopper. Section 4 concludes the paper. We

recommend that extending an existing methodology

to model each required property in an agent system

is preferred, instead of developing a methodology

from scratch for each new property to be considered.

2 mGAIA

We present mGaia as an enhancement of Gaia to

facilitate conceptual modelling of mobile agent

systems. In order to support conceptual modelling of

mobile multiagent systems, mGaia incorporates the

existing models of Gaia and adds a new model,

namely, the mobility model. Figure 1 shows the

structure of mGaia’s models. The basic ideas of

mGaia are borrowed from the existing Gaia

methodology. As such, mGaia still consists of the

analysis and design phases. The objective of the

analysis phase is to obtain an understanding of the

system and its structure. It consists of the roles

model, which identifies the roles in the system and

the interaction model, which identifies the

interactions between the roles found.

There are four attributes of roles:

515

Sutandiyo W., Baruwal Chhetri M., Wai Loke S. and Krishnaswamy S. (2004).

MGAIA: EXTENDING THE GAIA METHODOLOGY TO MODEL MOBILE AGENT SYSTEMS.

In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 515-518

DOI: 10.5220/0002627205150518

 SciTePress

Requirements Statement

Interaction Model

Roles Model

• Responsibilities that consist of the liveness

property, which defines the continual execution

of the role within the system, and the safety

property, which is a condition that must be

maintained to avoid system behaviour that is

contrary to its system requirements,

• Permissions, which define the access privileges

or rights of roles,

• Activities, which are tasks that need to be

performed by roles without interaction with

other roles, and

• Protocols, which are activities that involve

interactions with other roles.

The objectives of the design phase are to convert

the system from an abstract level to a concrete level

and to ease implementation. The design phase

consists of the agent model, the services model, and

the acquaintance model and the new mobility model.

• The agent model of mGaia is used to identify

the agent types and how many agents are

involved within the entire system. It includes

constructs to distinguish between agents that

possess the characteristic of mobility and those

that do not.

• The services model of mGaia is the list of

services that each role can provide and be

associated with.

• The acquaintance model defines the

communication links between each agent.

Abstract Concepts Concrete Concepts

Roles

Permissions

Responsibilities

Protocols

Activities

Liveness Properties

Safety Properties

Agent types

Services

Acquaintances

Place Types

Atomic movement

Travel paths

Table 1: Abstract and Concrete Concepts in mGaia

• The mobility model of mGaia defines the

mobility characteristics of agents further, such

as identifying the movements and travel path of

each mobile agent.

Like Gaia, mGaia has abstract concepts and

concrete concepts. The abstract concepts are used

during the analysis process and they do not

necessarily have direct correlations in the run-time

system. The concrete concepts are considered during

the design process. The concrete concepts have

direct correlations in implementation of the run-time

system. Table 1 summarises the abstract and

concrete concepts of mGaia. It must be noted that in

Table 1, the italicised concepts are unique to mGaia

and mainly aim to support modelling agent mobility

in multiagent system. In summary, the additional

features of mGaia involve modifications to the

following Gaia’s models: role model, agent model

and the introduction of a new mobility model. We

now present analysis and design phases of mGaia.

2.1 Analysis Phase of mGaia

In mGaia, modifications have been made to roles

model so that each role identified is categorized into

three different role types - system, interface, and

user roles. The purpose of categorising roles is to

clarify each role’s responsibilities within the system.

A system role is defined as a role that interacts with

other parts of the system and not the user. An

interface role is a role that interacts with the user and

the other parts of the system. A user role is a role

that represents the human user itself. Despite the

modification in the roles model, the remaining

components are the same as in Gaia.

Acquaintance Model

Services Model

Analysis

Agent Model

Desi

ure 1: Structure of mGaia’s

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2.2 Design Phase of mGaia

In mGaia we have modified Gaia’s agent model to

specify the mobility characteristic of agents. We

have also introduced the new mobility model. The

following section will discuss the modified agent

model and the mobility model.

2.2.1 The Agent Model

The agent model identifies the number of agents, the

agent types, and the relationship between the roles

identified (in the role model) and the agent types in

the system.

Figure 2: Agent model of mGaia

mGaia’s agent model classifies the agents into two

different categories - mobile (by adding a notation of

“m” sign) and stationary. The categorisation of agent

types caters for mobility characteristic of agents.

Furthermore, we modify the agent model to allow

similar behaviour roles to be grouped into one

category. This is notational illustrated by grouping

the role names between parentheses as shown in

Figure 2. This modification is for convenience of

presentation.

2.2.2 Mobility Model

The mobility model enhances Gaia to incorporate

support for modelling of mobile agents in multiagent

systems. While the analysis phase of mGaia involves

identifying the roles and the interactions of each

role, the design phase of mGaia involves agents.

Therefore, the mobility model is best fitted into the

design phase rather than in the analysis phase, as

mobility is a characteristic of agents and not roles.

Furthermore, mobility is not an interaction, as an

agent does not need to be mobile to communicate.

These considerations motivated the inclusion of the

mobility model in the design phase. The mobility

model is derived from the agent model. In the agent

model, the agent types are categorised into mobile

and stationary. In order to model the mobility

characteristics of mobile agents, the mobility model

identifies place types. There are four steps in

constructing the mobility model:

Step 1: Place Types

Identify the place types, which are locations that

the mobile agent can visit or reside in. Table 2

shows the place types in the mobility model of

mGaia. Table 3 identifies the instance operators of

place types.

Table 2. Place Types

Place Types Description Instances

Short

English

description

of place

types

Instance o

erator

indicates how

many place types

exist in the s

ste

Table 3. Cardinality Operators in mGaia

Operator Description

m……n

Exactly n instances

Between m and n instances

0 or more instances

1 or more instances

Step 2: Agents and Places Specifications

Step 2 of the mobility model is derived from step

1 and the agent model. In this step, we identify the

relationship between agent types and place types. It

also defines the constraints of the relationship. The

agents and places specifications are derived from the

place types identified in step 1 of mobility model.

Table 4 shows the agent and place specifications for

the mobility model.

Table 4. Agent and Place Specifications

Agent

Types

Mobile Place

Types

Constraints

A tick sign to

identify if the

specific agent

is mobile or

not

P1,

The

constraints of

agents and

place types

relationship

Step 3: Cardinality of Agents and Places

The cardinality between agent types and place

types shows how many agents of an agent type can

reside in a place of a place type. The cardinality of

agents and places (step 3) is based on the agents and

places specifications (step 2).

A symbol for agent type

m mark for mobile agent

I symbol for role name

R symbol for roles names that are grouped

into one role name

x instance qualifiers of agent model

, r

,…..r

) R

MGAIA: EXTENDING THE GAIA METHODOLOGY TO MODEL MOBILE AGENT SYSTEMS

517

Figure 3. Cardinality of Agents and Places

Step4: Travel Schema of Mobile Agent Types

The travel schema of each mobile agent type

includes origin, final destination, list of atomic

movements, and paths. The origin is the place type

where the mobile agent starts the movement to

accomplish the tasks assigned. The final destination

is the place type where mobile agent will reside after

it completed the tasks assigned. The atomic

movement is the smallest granularity movement

required to accomplish the tasks assigned. The paths

are the list of atomic movements that the mobile

agent may travel in order to accomplish the tasks

assigned.

3 OVERVIEW OF THE SMART

LECTURE THEATRE SYSTEM

The Smart Lecture Theatre system is a multiagent

system, where a different type of agent manages

each type of interaction. Within the Smart Lecture

Theatre architecture, agents will be stationary and/or

mobile. Each agent will have a task assigned and it

will seek to perform and fulfil the task assigned. For

example, a student needs to find out the contact

details of lecturer A. This student will query from

his/her user device. First the student is required to

unique user agent handles this operation. Once the

query has been triggered, the user agent creates a

query agent automatically. The query agent with the

assigned task (i.e. “What are the contact details of

lecturer A?”) will migrate to the lecture theatre and

attempt to answer the student’s query. The design of

Smart Lecture Theatre requires the mobile agent to

move from the user device to the lecture theatre to

accomplish the task assigned. There are three types

of users in Smart Lecture Theatre system namely

student, lecturer and administrator. Each particular

user has different types of services available.

Student can query lecturer details, such as lecturer’s

URL address, lecturer’s room number and email

address, and query unit details, such as unit’s name

and unit’s URL address. The administrator performs

update of the lecture theatre features, such as

disabled access, capacity, speakers, OH projectors

and LAN connections and also the user details, such

as username, real name, URL and user type. The

Lecturer can list lecture theatres based on the

features such as disabled access, capacity, speakers,

OH projectors and LAN connection, query lecture

theatres on available times and book the specified

lecture theatre at a particular campus and negotiate

with another user when the other user has booked

the lecture theatre for a particular time slot. The

analysis and design of the Smart Lecture Theatre

system was done using mGaia and the

implementation was performed using the

Grasshopper mobile agent toolkit.

Cardinality

Operator

4 CONCLUSION AND FUTURE

WORK

We have presented a conceptual modelling

methodology for mobile agent systems. We

presented our experiences in mapping the mGaia

models to Grasshopper mobile agent toolkit

(http://www.grasshopper.de) in (Sutandiyo et al.,

2004), which showed mGaia to be effective. These

experiences have indicated that there are several

open issues that need to be addressed which are the

focus of our current work. The key issues include

identification of additional constructs for mobile

agent systems, formalization of the constructs,

specification of mobility of agent contexts/places

and addressing the mobility of roles – or mobility in

the analysis phase of the modeling.

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