STUDY OF AGENT ASSISTED METHODOLOGIES FOR DEVELOPMENT OF A SYSTEM

International Journal of Technical Research and Applications e-ISSN: 2320-8163,
www.ijtra.com Volume 3, Issue 2 (Mar-Apr 2015), PP. 62-66
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STUDY OF AGENT ASSISTED METHODOLOGIES
FOR DEVELOPMENT OF A SYSTEM
Reshma Kazmi, Brijesh Pandey, Namarata dhandha
(Goel Institute of Technology and Management)
Abstract. Agent Assisted Methodologies have become an
important subject of research in advance Software Engineering.
Several methodologies have been proposed as, a theoretical
approach, to facilitate and support the development of complex
distributed systems. An important question when facing the
construction of Agent Applications is deciding which
methodology to follow. Trying to answer this question, a
framework with several criteria is applied in this paper for the
comparative analysis of existing multiagent system
methodologies. The results of the comparative over two of them,
conclude that those methodologies have not reached a sufficient
maturity level to be used by the software industry. The
framework has also proved its utility for the evaluation of any
kind of Agent Assisted Software Engineering Methodology.
I. INTRODUCTION
The aim of state of the art is to provide an overview of the rapidly
evolving area of multi-agent systems (MAS) and its related
methodologies. This leads to a discussion of what makes an agent
oriented methodologies that can be used to build MAS. In this
chapter literature concerning agent systems and agent oriented
methodologies is reviewed in detail. The field of agent oriented
methodologies is examined. The aim is establishing the
characteristics of agent oriented methodologies, and seeing how
these characteristics are suited to develop multi-agent systems. An
analysis of agent oriented methodologies is examined giving a
clearer picture of their application domain, advantages and
limitations.
Designing and building high quality industrial-strength
software is difficult. Indeed, it has been claimed that such
development projects are among the most complex
construction tasks undertaken by humans. Against this
background, a wide range of software engineering paradigms
have been devised (e.g., procedural programming, structured
programming, declarative programming, object-oriented
programming, design patterns, application frameworks and
component-ware). Each successive development either claims
to make the engineering process easier or to extend the
complexity of applications that can feasibly be built. Although
there is some evidence to support these claims, researchers
continually strive for more efficient and powerful software
engineering techniques, especially as solutions for ever more
demanding applications are required.
This paper will argue that analyzing, designing and
implementing software as a collection of interacting,
autonomous agents (i.e., as a multi-agent system represents a
promising point of departure for software engineering. While
there is some debate about exactly what constitutes an
autonomous agent and what constitutes interaction, this work
seeks to abstract away from particular dogmatic standpoints.
Instead, we focus on those characteristics for which there is
some consensus. From this standpoint, the paper’s central
hypothesis will be advanced: for certain classes of problem
(that will be defined), adopting a multi-agent approach to
system development affords software engineers a number of
significant advantages over contemporary methods. Note that
we are not suggesting that multi-agent systems are a silver
bullet there is no evidence to suggest they will represent an
order of magnitude improvement in software engineering
produc2tivity. However, we believe that for certain classes of
application, an agent-oriented approach can significantly
improve the software development process.
In seeking to demonstrate the efficacy of the agent-
oriented approach, the most compelling form of analysis
would be to quantitatively show how adopting such techniques
had improved, according to some standard set of software
metrics, the development process in a range of projects.
However, such data is simply not available (as it is still not for
more established methods such as object-orientation).
However, there are compelling arguments for believing that an
agent-oriented approach will be of benefit for engineering
certain complex software systems. These arguments have
evolved from a decade of experience in using agent
technology to construct large-scale, real world applications in
a wide variety of industrial and commercial domains.
The contribution of this paper is twofold. Firstly, despite
multi-agent systems being touted as a technology that will
have a major impact on future generation software (“pervasive
in every market by the year 2000” and “the new revolution in
software”), there has been no systematic evaluation of why
this may be the case. Thus, although there are an increasing
number of deployed agent applications for a review, nobody
has analysed precisely what makes the paradigm so effective.
This is clearly a major gap in knowledge, which this paper
seeks to address. Secondly, there has been comparatively little
work on viewing multi-agent systems as a software
engineering. This shortcoming is rectified by recasting the
essential components of agent systems into more traditional
software engineering concepts, and by examining the impact
on the software engineering life-cycle of adopting an agent-
oriented approach.
II. THE NATURE OF COMPLEX SOFTWARE
SYSTEMS
Industrial-strength software is complex in nature: it is
typically characterised by a large number of parts that have
many interactions. Moreover this complexity is not accidental
[4]: it is an innate property of the types of tasks for which
software is used. The role of software engineering is therefore
to provide structures and techniques that make it easier to
handle this complexity. Fortunately, this complexity exhibits a
number of important regularities:

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 Complexity frequently takes the form of a
hierarchy1. That is, the system is composed of inter-
related sub-systems, each of which is itself a
hierarchy. The precise nature of the organisational
relationships varies between sub-systems, although
some generic forms
 (such as client-server, peer, team) can be identified.
Organisational relationships are not static: they can,
and frequently do, vary over time.
 The choice of which components in the system are
primitive is relatively arbitrary and isdefined very
much by the observer’s aims and objectives.
 Hierarchic systems evolve more quickly than non-
hierarchic ones of comparable size. In other words,
complex systems will evolve from simple systems
much more rapidly if there are stable intermediate
forms, than if there are not.
 It is possible to distinguish between the interactions
among sub-systems and the interactions within sub-
systems. The latter are both more frequent (typically
at least an order of magnitude more) and more
predictable than the former. This gives rise to the
view that complex systems are nearly decomposable.
Thus, sub-systems can be treated almost as if they are
independent of one another, but not quite since there
are some interactions between them. Moreover,
although many of these interactions can be predicted
at design time, some cannot.
III. AGENT ORIENTED METHODOLOGIES
In order to be able to perform a comprehensive literature
review for the agent oriented methodologies, "what the meaning of
the methodology is" needs to be precisely defined before starting
this discussion. A good methodology should provide the models
for defining the elements of the multi-agent environment (agents,
objects and interactions). A good methodology should also
provide the design guidelines for identifying these elements, their
components and the relationships between them. Any
methodology aims to provide a set of guidelines that covers the
whole lifecycle of the system development. The guidelines should
cover both the technical as well as the management aspects.
It is also important for a methodology to provide notations
which allow the developers to model the target system and its
environment. In addition to the methodology, there are also tools
that support the use of such methodologies. For instance, diagram
editors help developers drawing symbols, models described in the
methodology. The Rational Unified Process (RUP) is a good
example of a software engineering methodology (Kruchten 2000).
It uses the notation described in the Unified Modelling Language
(UML) (Booch 1998) and its typical tool support is Rational Rose.
IV. GAIA METHODOLOGY
Gaia methodology (Wooldridge 2000) was developed by
Wooldridge et-al. for analysis and design of agent systems.
Gaia is enhanced by Zambonelli in 2003. Gaia is a general
methodology that supports both levels of Micro and Macro
development of agent systems. Micro level relates to agent
structure while Marco level relates to agent society and
organizational structure. Gaia includes an analysis and design
phase and does not explicitly support an implementation
phase.
Gaia starts with the analysis phase as is given in figure 1,
whose aim is to collect and organize the specification. This is
the basis for the design of the computational organization.
Then it continues with the design phase, which aims to define
the system organizational structure. The definition is in terms
of the system’s topology and control system to identify the
agent model and service model.
Subdivide system into
Sub-organization
Preliminary
Role Model
Organizational
Rules
Preliminary
Interaction Model
Organizational
Structure
Catalogue of
Organizational
Patterns
Interaction
Model
Roles Model
Agent Model Services Model
Analysis
Phase
Design
Phase
Environmental
Model
Figure 1 Gaia methodology Models
V. HLIM METHODOLOGY
HLIM methodology is considered to be one of the
important agent oriented methodologies. It was developed in
1999 by M. Elammari and W. Lalonde. It allows the
development of agent based systems from user requirements.
The methodology models the external and internal behavior of
agents. It also provides a means for both the visualization of
the behavior of systems’ agents and the definition of how the
behavior is achieved. The methodology provides a systematic
approach for generating system definitions from high-level
designs which can be implemented. The methodology captures
effectively the complexity of agent systems, agents’ internal
structure, relationships, conversations, and commitments.
Figure 3.3 shows the models of HLIM methodology. The
HILM methodology consists of two phases (the discovery
phase and definition phase). The discovery phase is an
exploratory phase that leads to the high-level model definition.
The agents are discovered and their high-level behaviour is
identified. The discovery phase includes only the high-level
model.

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Figure 2 HLIM methodology Models
VI. THE PASSI METHODOLOGY
The PASSI methodology is considered to be one of the
important agent oriented methodologies that are object
oriented based methodologies. It was developed in 2002 by
Cossentino and Potts. PASSI is composed of five models that
address different design concerns and twelve steps in the
process of building a model.
PASSI uses UML as the modelling language because it is
widely accepted both in the academic and industrial worlds. Its
extension mechanisms facilitate the customized representation
of agent-oriented designs without requiring a completely new
language. Extension mechanisms here refer to constraints,
tagged values and stereotypes. The models and phases of
PASSI are (see figure 3):
Figure 3 models and phases of the PASSI methodology
VII. TROPOS METHODOLOGY
The Tropos methodology is considered to be one of the
important agent oriented methodologies. It was developed in
2003 by J. Castro, M. Kolp, and J. Mylopoulos.
The Tropos methodology is intended to support all analysis
and design activities in the software development process, from
application domain analysis down to the system implementation.
It takes into account both inter-agent and intra-agent issues. In
particular, Tropos rests on the idea of building a model of the
system-to-be and its environment that is incrementally refined
and extended. Tropos methodology provides a common interface
to various software development activities, as well as a basis for
documentation and evolution of the software. Tropos
differentiates between an early and a late requirements phase,
and between architectural design and detailed design. Tropos
consists of the following three main phases: Requirement
analysis phase, Design phase and Implementation phase.
Figure 4 illustrates the models of Tropos methodology.
Early Requirement phase
Late Requirement phase
Requirement Analysis Phase
Architectural Design
Detailed Design
Design Phase
Implementation Phase
Figure 4 models and phases of the Tropos methodology

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VIII. PROPOSED METHODOLOGY
Figure 5 MBA Model
Eliminates the shortcomings faced by most other existing
methodologies. The second step is that we demonstrate the
new proposed methodology through the use of a case study
representing a project clearance system. This case study refers
to the collection of detailed information about how the
proposed methodology works.
IX. MAB MODEL METHODOLOGY
We represent the proposed methodology MAB model. This
methodology is constructed in order to as a reliable systematic
approach that proves a milestone for software development
lifecycle .The proposed methodology covers the most
important characteristics of multi-agent systems.
The new methodology deals with the agent concept as a high
level abstraction capable of modeling the complex system. The
new methodology also include well known techniques for
requirement gathering for user agency communication and
links them to domain analysis and design models such as
UCMS,UML Use Case Diagrams, Activity diagrams , FIPA-
ACL, etc. Furthermore, it supports simplicity and ease of use
as well as traceability.
MAB model is composed of four main phases, a System
requirements phase, an analysis phase, a design phase and an
implementation phase. Figure illustrates the detail of MAB
methodology. The next few sections present a more detailed
discussion of each of the four phases.
SYSTEM SCENARIO MODEL: the model is designed with
the help of the use case map and use case diagrams to define
the scenario of the system .In this case study we have designed
the project clearance scenario. Where the system scenario is
discussed in detailed.
ROLE MODEL: this model defines the role of the agent
within the system. In the project clearance system the agents
are identified and their rolesdefined for e.g, The role of the
DFO within the system is to verify the details of the project
and then forward it to the Ministry or return it back to the User
agency for further details.
BELIEFS MODEL: here the agent’s beliefs are discussed,
i.e. the area or jurisdiction of the particular agent and their
specific belief in persuing that tasks. i.e. all the agents that are
playing an important role in the project clearance.Theymay be
the DFO, Local Bodies and many others who have their own
perspective of judging it.
AGENT MODEL: All the agents that pay an important role
in MAB architecture. Here the agents work is to judge the
proposals and forward them to the higher authorities for
decision after marking their remarks.

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X. CONCLUSIONS
In this article, we have described why we perceive agents
to be a significant technology for software engineering. We
have discussed in detail how the characteristics of certain
complex systems appear to indicate the appropriateness of an
agent-based solution: as with objects before them, agents
represent a natural abstraction mechanism with which to
decompose and organize complex systems. In addition, we
have summarized some of the key issues in the specification,
implementation, and verification of agent-based systems, and
drawn parallels with similar work from more mainstream
computer science. In particular, we have shown how many of
the formalisms and techniques developed for specifying,
implementing, and verifying agent systems are closely related
to those developed for what are known as reactive systems in
mainstream computing. Finally, we have described some of
the pitfalls of agent-based development. Software engineering
for agent systems is at an early stage of development, and yet
the widespread acceptance of the concept of an agent implies
that agents have a significant future is software engineering. If
the technology is to be a success, then its software engineering
aspects will need to be taken seriously. Probably the most
important outstanding issues for agent-based software
engineering are: (i) an understanding of the situations in which
agent solutions are appropriate; and (ii) principled but
informal development techniques for agent systems. While
some attention has been given to the latter (in the form of
analysis and design methodologies for agent systems, almost
no attention has been given to the former
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