DEVELOPMENT OF A MULTIAGENT BASED METHODOLOGY FOR COMPLEX SYSTEMS

International Journal of Technical Research and Applications e-ISSN: 2320-8163,
www.ijtra.com Volume 3, Issue 2 (Mar-Apr 2015), PP. 71-75
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DEVELOPMENT OF A MULTIAGENT BASED
METHODOLOGY FOR COMPLEX SYSTEMS
Reshma Kazmi, Brijesh Pandey, Namarata dhandha
Goel Institute of Technology and Management
Abstract- Multiagent Based 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 Multiagent Based Software Engineering Methodology.
I. INTRODUCTION
In our research, we view Multiagent Software
Engineering as a further abstraction of the object-oriented
paradigm where agents are a specialization of objects. Instead
of simple objects, with methods that can be invoked by other
objects, agents coordinate with each other via conversations
and act proactively to accomplish individual and system-wide
goals. Interestingly, this viewpoint sidesteps the issues
regarding what is or is not an agent. We view agents merely as
a convenient abstraction, which may or may not possess
intelligence. In this way, we handle intelligent and non-
intelligent system components equally within the same
framework. In addition, since we view agents as
specializations of objects, we build on existing object-oriented
techniques and apply them to the specification and design of
multiagent systems.
The primary focus of MaSE is to help a designer take an
initial set of requirements and analyze, design, and implement
a working multiagent system. This methodology is the
foundation for the Air Force Institute of Technology's (AFIT)
agentTool development system, which also serves as a
validation platform and a proof of concept. The agentTool
system is a graphically-based, fully interactive software
engineering tool for the MaSE methodology. agentTool
supports the analysis and design in each of the seven MaSE
steps. The agentTool system also supports automatic
verification of inter-agent communications and code
generation for multiple multiagent system frameworks. The
MaSE methodology, as well as agentTool, is independent of
any particular agent architecture, programming language, or
communication framework. The focus of our work is on
building heterogeneous multiagent systems. We can
implement a multiagent system designed in MaSE in several
different ways from the same design.
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.
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, realworld 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, nobody has analysed

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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. PROPOSED METHODOLOGY
Figure 1 MAB Methodology
Plans Model: What are the plans and targets that have been
set that should be used to achieve the goal of the agent. The
main plan is to save the forest area and if required then make
suitable arrangements for replantation of the trees along the
project areas or blank spaces.
Goal Model: Goals which have to be achieved by the agents
while working within the system. i.e, less damage to the forest
area and more development.
Triggers Model: This model marks the events and change of
beliefs that occur in the system. This may represent the
different point of view of visualizing particular things which
act as a trigger in the project.
Agent Service Model: Here the responsibilities and
commitments of each agent is described and marks that it is
fulfilled by all agents within the system for the smooth
functioning of the system. i.e. the different agents work
together for the development of the nation keeping in mind
that the forest are to be saved and alternatives to be worked out
before clearance is accorded.
Agent Container: The different behavior of various
agentsvwithin the system design. Different role of agent in
different areas. Different roles and duties of a particular
officer.
Directory Facilitator Model: The default services of the
agents within the system are defined. This defines the roles of
the agents in the ideal stage. Default roles and duties of a
particular officer.
Inter Agent Communication Model: Different
communication protocols between the system and agents. this
model takes care of the inter communication between
different agents within a particular system. Or inter
communication of the agents within the system and from
externals taking into consideration the communication
protocols.
System Requirements Phase
The system requirements phase guides towards the detection
of the system components and their high level behavior
.System components are like (objects, roles , resources etc)
The system requirements phase is concerned with the
description of the system scenario using well known
techniques. The known techniques are such as Use-Cases
Diagrams (UCDs), and UML use case diagrams.The system
requirements phase is composed of the following model
In this model the scenarios of the system as a whole are
described .The description includes components that the
system is composed of and the tasks that have to be performed

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by each component in the system. How these components
interact with each other and with the external environment is
also illustrated. To fulfill this description tasks, some specific
well-known techniques have been such as User-Case
maps(UCMs) and Use-Case diagrams(UCDs).
The Use-Case maps(UCMs) techniques is a precise structure
notation .It describe the general behavior of the system in the
form of scenarios without reffering to any implementation
details. UCMs include adequate information in a summarized
form. It has two advantages.
1) It enables developers to understand and conceptualize the
behavior of the system as a whole.
2) It gives an explicit concept about how the system operates
as a whole.
In the system scenario model, User-Case diagrams will also
exploited. User-Case diagrams are an UML notation which
describes the behavior of the system from the user point of
view. It is through this notation that the roles in the system can
be recognized .Recognition of roles within a system is a very
helpful during the analysis and design phases as well as for
understanding the system’s requirements.
Analysis Phase
The analysis phase is concerned with the description of agent
architecture and AAS. It is divided into two parts.The first part
deals with agent architecture. The second part deals with AAS
architectures.
The next section provides a detailed description of both
architectures. The agent architecture step describes the internal
structure of agents in the system. On the other hand AAS
architecture step describes the relationship between the agents,
the conversation and exchanged messages and agent
services.This description is important in order to facilitate two
main functions:
1) To enable negotiation and cooperation between agents.
2) To establish commitments and agreements that the agents
should adhere to in order to provide the services to other
agents in the system.
Agent Architecture
This step is concerned with the description of agent’s internal
structures. It describes the following:
1) Roles that the agent should play or perform in the system.
2) Agents that should exist in the system.
3) Goals and plans that each agent should have within.
4) Triggers that each role should be aware of as being events
that take place in the system.
5) Roles Model
In this model the roles that an agent will play in the system
will be stated. The important attributes of each role such as
responsibilities, permissions, perceptions; obligations and
constraints will be described in details.
Responsibilities are the activities that the role is responsible to
perform. Permissions are the authorities related to numbers
and types of resourses that will be exploited by agents in the
system. Obligations are requirements that should be available
to enable the role to start its functionality and carry out its
responsibilities and activities. Constraints are restrictions and
boundaries that the role must not infringe through executing
its tasks.
Agent Model
In the agent model the internal description of agents within the
system are illustrated .The internal structure of an agent is also
described. In this methodology each agent possesses a goal or
more, which it desires to realize .In addition an agent will also
possesses beliefs that it depends on to achieve its goals. These
beliefs can be considered as preconditions to initiate the
achievement of goals for the system agents.
Agent Goals Model
In the agent goals model the goals that the agent desires to
achieve will be identified.
Each goal and its priorities will be identified. Each goal will be
initiated according to a specific priority. The plans which are
prepared by the agent to satisfy the desired goal will also be
identified. This model also contains preconditions and post
conditions to initiate the process of achieving goals that the
agent desires to realize.
Plans Model
In the plans model the plans that have to be performed or have
to be followed by an agent during achieving a specific goal are
recognized. In other words, every goal has to be achieved
through a specific plan or more. Plans may execute in a
sequential manner or according to the priority of each plan or
in parallel manner. This model describes the plan as a set of
tasks executed by the agent. Also includes the names of
interaction protocols that take place between agents in the
system.
Beliefs Model
The agent knowledge is considered as one of the most
important parts of MAB model methodology. It stores relevant
facts about the agent and its environment. Agent knowledge
may be taken to explicitly represent the agent’s beliefs about
its environment or even about itself or about its environment
or even about itself or about other agents. The beliefs model in
MAB model methodology is carried out by following the
scenario of UCMs. This is followed by the transfer of those
scenarios into beliefs according to a specific goal or a specific
plan or both.
Agent Triggers Model
This model describes the mechanism of how the agent
perceives its environment through percepts and how to act on
it through actions. Percepts are information coming from the
environment which has an effect on the behavior of agents.
According to that information the role performs some actions
as a reaction. Actions are the mechanism through which an
agent effects its environment.
There are events in AAS which occur during the system
runtime. There may also be change in agent’s beliefs. These
events and changes in beliefs will trigger some affected agents
to take some actions or reactions as a response to those events
or changed in beliefs. These events and these changes in
beliefs are called triggers. For if these triggers occur in the
system then obviously there must be a party responsible for
generating them. Triggers can be generated by an agent, an
object or some by other party. Any party in a system
generating a trigger is known as a source. There is also another
party in the system which is the party that benefits from the
operation. This is called the beneficiary. The beneficiary
represents the agent who will react to this trigger. All expected

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events and changes in beliefs in the system can be recognized
through this model.
III. MAB ARCHITECTURE
This step is concerned with the description of AAS
structure. It describes the relationships between the agents in
the system, the conversation and exchanged messages. All this
enable negotiation and cooperation between agents, the
commitments and agreements that agents should adhere to in
order to provide the services to other agents in the system.
Agent Interaction Model
An agent interaction model contains a description of the
interactions between agents in the system where inter-
communication among agents is performed. Therefore,
interaction diagrams were adapted from object oriented design
techniques, and allocated
To agents instead of objects.
The interaction diagrams from the system scenario model will
be developed by capturing the lines that connect agents inside
a use-case maps diagrams and transform them to conversations
within the system. This model represents the primary step of
Inter-agent communication model.
Agent Relationship Model
Agent relationship model is a set of system agents connected
together to satisfy and pursue a common goal. This model
consists of all system agents with the relationships,
dependencies and authorities between system’s agents being
clearly described. The constraints and restrictions that a system
must not encroach will also be described.
Agents Services Model
Agent services model provides a standard mean of inter-
operating between different agent in the system. This model is
intended to provide a common description of an agent
services. The model is intended to provide a common
description of an agent services. The model is also intended to
define the place of the agent services within an agent system.
This guides the agent community to those services easily. A
services is realized by an agent and is used by another agent.
Agent services are captured by means of the messages
exchanged between requester agents and provider agents. The
main goal of the agent services model is to facilitate access to
sevices that are offered by each agent. Also, it organizes the
cooperation between agents through constructing formal
agreement. An agreement maintains agent’s rights by
providing them the ability to obtain those services in time.
Design Phase
The design phase is concerned with the detailed representation
of the models developed in the previous phases and
transforming them into patterns. These patterns are useful for
actually implementing the multi-agent system. This phase
captures key activities including agent structural design,
development strategy and System design specifications. The
design phase has three steps:
1) Creating agent container.
2) ConstructingInter-agent communications.
3) Creating Directory facilitator.
4) Agent Container Model
The agent container will describe the overall system
organization which is composed of agent classes and the
conversations between them. The agent’s behavior is defined
in terms of a container representing agent’s roles in the system
and the conversations in which they participate.
Inter –Agent Communication Mode
This model defines in details interactions among agenta in the
system where communication between agents is established.
To perform this communication between agents, agreed and
accepted protocols have to be defined. these protocols are
related with exchanged knowledge between agents in the
system. Therefore we will exploit Agent Communication
specifications.
Directory Facilitator Model
Directory facilitator model is responsible for providing the
equivalent of a yellow pages directory service to ther agents in
the system. Agents may register their services at the directory
facilitator (DF) or query the DF to find out what services are
offered by other agents. An agent is responsible to provide
information related to service e. g. service type, service name
etc. Furthermore, an agent can also deregister or modify its
service details. Any agent can interact with DF to make its
services public and to identify agents that provide a particular
service.
Implementation Phase
Case Study: Project Clearance System
A brief description of how the project clearance system works
which represent the case study to test and evaluate the new
methodology.
The case study “Project Clearance System” has been chosen
because it is simple and straight forward. It can be used to
illustrate the type of reflective reasoning required by agents
involved in a distributed collaborative environment. It entails a
distributed design process, where several participants needed
to interact with each other. It encompasses and highlights a
number of underlying and interconnected agent concepts.
Project Clearance System
Project Clearance System was designed for Ministry of
Environment & Forest, Central Region. In the regional office
there are two sections one is the Environmental section which
monitors the projects that have been accorded Environment
Clearance and the second section i.e, is the forest is there
accord forest clearance under FC Act 1980 to the projects
submitted by the user agencies.
The services provided by the forest department
1) Different types of clearance to the user agencies for the
constructions of various projects.
2) The user agency can submit the proposal online and being
processed at different levels it is submitted to the Ministry for
final action.
3) Projects once submitted is then evaluated, any further
queries are required that is asked from the concerned authority.
4) If documents evaluated are up to the mark and that project
is required for that place it is accorded clearance and the user
agencies can start with the projects.
5) The projects submitted by the user agency are intimated
with the decision of the Ministry with a set time frame
required for the processing of the project.
6) Project Clearance System has also many bad experiences
with the user agencies such as improper information, fake
information, hiding the facts. In this phase, we deal with a part

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of Project Clearance System behavior, triggered by the
following kinds of events:
 Accepting projects for the clearance of new and
existing User agencies on various locations.
 According clearance on the set rules and conditions.
 Providing the best services to the user agencies, by
providing them with the progress of the objects
submitted for clearance through their site and written
communication.
 Handling the user agencies more thoroughly with the
whom they found errors in.
The case study is considered to be applied with both UCMs
and UCDs.
Implementation phase
The implementation (or construction) phase is the point in the
development process. When we actually start to construct the
solution. This is the time to start writing the program code. If
the methodology process was followed so far, then a group of
model would have been contructed. They will provide a lot of
guidance for the implementation phase. The models have a
complete set of design specification showing how the agent
system and its components should be structured and organized.
The next step would be to start handling out the various design
specifications and start to build the implementation code step.
Oracle & Developer Platform
The oracle Database (commonly reffered to as RDBMS or
simply as oracle) is an object-relational database management
system (ORDBMS) Produced and marketed by oracle
corporation. Oracle Database suite is a suite of development
tools realesed by oracle corporation. The principal components
were initially Oracle Forms and Oracle Reports .The
Developer interface became more similar over time and they
were eventually group together as Oracle IDE (integrated
Development Environment). Oracle Forms is a software
product for creating screens that interact with an Oracle
database. It has an IDE including an object navigator, property
sheet and code editor that uses PL/SQL. It was originally
developed to run server –side in character mode terminal
sessions .It was ported to other platforms, including Windows,
to function in a client-server environment. Later version were
ported to Java where it runs in a J2EE container and can
integrate with Java and Web sevices. Oracle Reports is a tool
for developing reports against data stored in an Oracle
database. Oracle Reports consists of Oracle Reports
developer(a component of the Oracle Develop Suite) and
Oracle Application Server Reports Services J Developer is a
freeware IDE supllied by Oracle Corporation .It offers
features for development in Java , XML , SQL and PL/SQL
,HTML, JavaScript, BPEL and PHP. J Developer covers the
full development lifecycle from design through coding,
debugging, optimization and profiling to deploying. With J
Develper, Oracle has aimed to simplify application
development in addition to building an advanced coding-
environment. Oracle J Developer integrates with the Oracle
Application Development Framework(Oacle ADF) . The IDE
platform also serves as the basis of another Oracle product,
SQL Developer, which Oracle Corporation promotes
specifically to PL/SQL-and database-developers.
IV. 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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