=Paper=
{{Paper
|id=Vol-57/paper-4
|storemode=property
|title=An Agent-Oriented Software Engineering Methodology with Application of Information Gathering Systems for LCC
|pdfUrl=https://ceur-ws.org/Vol-57/id-4.pdf
|volume=Vol-57
|authors=Tiemei Irene Zhang,Elizabeth Kendall,and Harvey Jiang
|dblpUrl=https://dblp.org/rec/conf/aois/ZhangKJ02
}}
==An Agent-Oriented Software Engineering Methodology with Application of Information Gathering Systems for LCC==
https://ceur-ws.org/Vol-57/id-4.pdf
An Agent-Oriented Software Engineering
Methodology with Application of Information
Gathering Systems for LCC
Tiemei Irene Zhang 1, Elizabeth Kendall 2, and Harvey Jiang 3
1
School of Network Computing, Monash University
McMahons Rd., Frankston, VIC. 3199, Australia
Irene.Zhang@infotech.monash.edu.au
2
Faculty of Information Technology, Monash University
McMahons Rd., Frankston, VIC. 3199, Australia
Kendall@infotech.monash.edu.au
3
Object Oriented Pty. Ltd., Level 11, 484 St. Kilda Rd.
Melbourne VIC. 3004, Australia
harveyj@oopl.com.au
Abstract. Life Cycle Cost (LCC) is a very important issue for organizations,
which determines the success of their businesses. However, information gather-
ing from a highly distributed heterogeneous environment with a huge number of
information sources is an obstacle to the use of the existing LCC models. To
overcome this obstacle, we took an agent-based information gathering system as
a solution. In order to develop an agent-based system in a systematic way, we
established a methodology of agent-oriented system engineering. Therefore, the
system development follows a step by step process from the stage of system
requirement to implementation. This paper presents this methodology and illus-
trates the processes of system analysis, design, and implementation by apply -
ing this methodology to information gathering for the CASA (Cost Analysis
Strategy Assessment) model. The experimental results show that the agent
technology is useful and beneficial for LCC information gathering.
1 Introduction
As cost is a key factor to determining the success of a product [6], manufacturers
attempt to reduce costs during every phase of the product’s life cycle [16]. They use
life cycle cost (LCC) models [23] to estimate the life cycle costs of their products b e-
fore making decisions. A typical model is CASA that is used in many organizations,
such as the U.S. Department of Defense. The CASA model covers the entire life of a
product and employs some 82 algorithms with 190 variables. Similar to the other LCC
models, CASA requires extensive information, manually gathered from different data
sources in a highly distributed and heterogeneous environment. however, this ap-
proach cannot deal with the need to respond to global economic competition, because
this environment involves unpredictable changes and uncertainty. Further, the Inter-
net is changing today’s business environment, and this also has a large impact on the
pro blem .
Having reviewed the available literature, we determined that an agent-based infor-
mation gathering system can potentially solve our problem. This is because an agent
is autonomous, social, reactive and proactive [26], and it can also model policies and
proactive behaviours [12]. In other words, agents provide high level communication
and interaction, which can perceive the situation of the environment and respond
appropriately. Agents are different from objects, which are static and cannot change
with the environment. For example, to gather information, an agent will first search its
own knowledge base. If the information is not available, it will be able to interact with
the other agents. An agent stores the info rmation in its knowledge base once it finds
it. If there are multiple data sources, an agent will be able to gather information in an
efficient manner. In contrast, it involves considerable difficulty and complexity to
realize the above scenario with object technology. So agent technology provides
perceived advantages over objects for solving our problem.
This paper aims to develop an agent -based system to gather information for the
CASA model. It establishes a methodology for analyzing and designing agents, and
then applies it to the CASA model [17]. We propose a conceptual model that takes
Infosleuth [19] as a reference and also we use the BDI (Belief, Desire, and Intension)
[20] agent as the agent architecture in our sys tem.
2 Literature Review
A number of methodologies have been reported to address agent-oriented software
engineering [24]. Wooldridge, Jennings and Kinny [27, 28] present the Gaia methodol-
ogy for agent-oriented analysis and design. Gaia is a general methodology that sup-
ports both the micro-level (agent structure) and macro -level (agent society and organi-
zation structure) of agent development. It requires that the inter-agent relationships
(organization) and agent abilities are known at run-time. Gaia includes analysis and
design processes. Gaia’s analysis process can find the roles in the system, and then it
models interactions between the roles that have been found. The design pro cess
maps roles into agent types, and then creates the right number of agent instances of
each type. Next, Gaia can be employed to determine the services needed to fulfill a role
in one or several agents, and to the final step involves creating acquaintance mo dels
for the representation of communication between the agents. The Gaia methodology
emphasizes a few models that can be utilised to form the whole system. It describes
what these models are, but the processes used to develop these models are vague. In
Gaia a role is viewed to be one of the roles in an organization, and role identification
itself is ad hoc.
Wood and DeLoach [5, 25] suggest the Multiagent Systems Engineering Method-
ology (MaSE). Simila r to Gaia, MaSE respects to generality and the application domain
supported, but in addition, MaSE goes further regarding support for automatic code
creation. It includes seven sections of capturing goals, applying use cases, refining
roles, creating agent classes, constructing conversations, assembling agent classes,
and system design. The identified roles are driven by the capturing goals. The goal of
MaSE is to lead the designer from the initial system specification to the implemented
agent system. Domain restrictions of MaSE are similar to those of Gaia’s, but in addi-
tion it requires that agent-interactions are one to one and not multicast
MAS-CommonKADS [9] is extends CommonKADS [21] for multi-agent systems. It
starts with a conceptualization phas e that is an informal phase for collecting the user
requirements. This methodology defines the models for the analysis and design of the
system, which includes the following models: agent, task, expertise, coordination,
organization, communication, and design. Although MAS-Common KADS employs
the notion of an agent's role, it does not formally define what this means. Also, the
concepts of role and class are used interchangeably [13] as the roles that used to an a-
lyze and design agents actually are the attributes of an agent class. The distinction is
important; a class stipulates the capabilities of an individual object, while a role fo-
cuses on the position and responsibilities of an entity in an overall structure or sy s-
tem. In particular, MAS-CommonKADS states that a CRC card describes an agent's
class.
Although the above methodologies use the concept of a role to design agents, no
formal techniques/representations, such as role models (role patterns), are used to
identify roles. This may lead to inappropriate behavior to appear in the agents (to
which roles are mapped) because roles must be clear and unambiguous to provide
detailed descriptions. This paper describes a methodology for designing agents that
is based on the use of role models.
3 Methodology
3.1 Overview
Object-oriented (OO) methodology with use cases has been widely used in software
development, and use case analysis has proved to be useful and successful for re-
quirement specification and analysis of OO systems. It is useful to investigate the use
of OO methodologies in agent-oriented software engineering. However, an agent is
autonomous, social, reactive and proactive [26], while an object does not possess
these cha racteristics. Therefore, we cannot directly apply the OO methodology to
agent-oriented software engineering. Current research in role models shows promising
results for software agent analysis and design [15]. Therefore, our methodology com-
bines these two approaches to develop an agent-based information gathering system
for product life cycle cost estimation.
To describe interactions between activities, an ICOM (input, control, output and
mechanism) presentation (as shown in Figure 1) is used to clarify constraints and re-
sources pertaining to an activity. This notation is adopted from the functional model
of IDEF [2, 3] that has widely been used for modeling manufacturing process.
Control
Input Output
Activity
Mechanism
Fig. 1. An activity model with ICOM notation
We apply the ICOM representation to depict the processes involved in our meth-
odology, as shown in Figure 2.
Identify Identify Determine Object Specification
Actors Objects Business Objects
Requirements Refine
Agent Specification
Identify Identify Assign Goals
Use Cases Roles to Assign
Responsibilitie and
s Compose
Conceptual Model Role Patterns Roles
Identify
Composite RRC Cards
Roles Identify
Develop Goal Role Composition
Agent Goals
Analysis and Role Patterns Cases and
Design Identify Beliefs
Fig. 2. Agent-oriented software engineering process
3.2 Object-Oriented Analysis
The object-oriented analysis depicted in Figure 2 consists of four activities: “Identify
Actors”, “Identify Use Cases”, “Identify Objects” and “Determine Business Objects”.
These four activit ies use the traditional methods developed by [10] to identify actors,
use cases, and objects. Note that an actor is a user who interacts with a sy stem or an
external system. A use case can be defined as a specific way of using the system by
performing some part of the functionality, and it includes preconditions, flow of
events, and post conditions. A use case model is closest to the requirements, and
with the least amount of detail [11]. Therefore, most business objects required by the
sys tem can be identified from the output of the “Identify Use Case” activity.
3.3 Goal Identification
The identified use cases are fed to the activity “Identify Goals” in Figure 2 for captur-
ing goals . A goal is an objective or a desired state that can be achieved or ascertained
by an agent. A goal identifies what is to be done , and it should change less often than
more detailed processes/activities. This is because a process or ac tivity identifies how
things are to be done . Goals are important to agent-based systems because agents are
autonomous and proactive. Agents achieve goals on the behalf of users through their
autonomous and proactive behavior. To identify goals from the use cases, we should
[14]:
• Identify the most top goal;
• Decompose it to the sub goals necessary to fulfill the top goal;
• Place the first set of sub goals as the first level goals ;
• Identify the next level of goals in a similar mode;
• Place them as the second level goals ;
• Derive all the goals in an iterative form;
• Stop when a goal cannot be structurally or temporally decomposed.
The result of goal identification is a goal hierarchy diagram where each level of
goals fulfils the goal on the level above. The identification of goals is an iterative
process because additional details may be uncovered and duplicated/unnecessary
items may be deleted, modified, or combined.
3.4 Goal Case Development and Belief Identification
Having identified goals, we next define a collection of scenarios about the agent’s
interactions in terms of the corresponding goals. Each scenario is called a goal-based
use case (in short, a goal case). This scenario describes a sequence of plans that ha n-
dle events that the agent initiates. An agent can start a goal case when the corre-
sponding goal is triggered. The use of goal cases also helps with traceability because
they are developed according to goals that link to the system requirements. To spe c-
ify the goal cases, the followin g steps are taken:
• Determine if the goal case is a reaction to an event or an outcome to be
achieved.
• Determine what triggers the goal case.
• Elaborate the context condition of the goal case.
• Determine the activities that have to be carried out for the goal case to be sat-
isfied
• Describe the conditions on the transitions.
• Determine the input data and output results.
• Determine the performance measures that need to be collected.
Goal cases are the core of agent specification. Once goal cases are developed, we
can assign them to agents according to the goals that each agent has. In our system,
the beliefs are the knowledge that agents have. When an agent wants to achieve a
goal and carry out a set of goal cases, the agent should have the knowledge to su p-
port its actions or evaluate the results. For example, the costing formulas are the be-
liefs of the agent who does the cost estimation . The beliefs can be identified and
extracted from the goal cases in terms of the knowledge and expertise that are the basis
for performing some activities.
3.5 Role Identification
The activity “Identify Roles” in Figure 2 occurs after “Identify Use Cases”. Here a role
involves a set of activities which, taken together, carry out a particular responsibility
or set of responsibilities. A role has the resources that are necessary for it to do its
activities. Those resources might reside permanently with the role or be passed to it.
Role names should be verbs in the gerund form. Roles can be identified from the rele-
vant role models, where role models are patterns of interaction and collaboration.
Many role models may appear in a given agent application. Because they are patterns,
they can be used during analysis and design as conceptual and analytical mo dels.
The activity of identifying roles from the use cases is to [15]:
• Examine role patterns from the existing role patterns literature and docume nta-
tion. Relevant role patterns can be used to identify or recognize types of in-
teraction and collaboration.
• Partition goals to form roles if there are no relevant role patterns that the goal
can be assigned to. This includes extracting the goals in a generic way and
taking these as the responsibilities of the role. Also, the other roles that col-
laborate with this role can be taken as collaborators. (In the next section, we
describe the relationships a role can have, along with responsibilit ies and col-
laborators .
• Determine all roles for the identified interactions and collaborations.
To document role models of agent sys tems, one important method is to use role re-
sponsibility and collaborations (RRC) [13] cards (refer to Table 1). These are used to
specify responsibilities and collaborations of roles, especially in the early phase of
agent-oriented software development.
Table 1. RRC card
Role model name
Role type Responsibility Collaborator
Names of Roles List all responsibilities List all collaborators
3.6 Assigning Goals to Responsibilities
After identifying RRC cards, we should assign goals as responsibilities of roles by
carrying out the activity “Assign Goals to Responsibilities”. This assignment starts at
the bottom of the goal hierarchy diagram. During this activity, a role is assigned goals
that are related to its responsibilities. Once the goals are assigned to responsibilities,
collaboration between the roles should be indicated. A RGC (Responsibility, Goal,
Collaborator) card, as shown in Table 2, is used to document relationships between
responsibilities, goals and collaborators for a role.
Table 2. RGC card
Role
Responsibilities Goals Collaborators
List of responsibilities List of goals List of collaborators
3.7 Assigning and Composing Roles
To design agents, we have to assign and compose the roles identified according to the
process in §3.5. When the roles are assigned and composed, their goals and collabo-
rators are allocated to th e agents . Note that goals and collaborators are obtained from
the RGC card shown in Table 2 whilst the goal cases and the beliefs have been deve l-
oped for each goal. The designated agents will carry out the roles in order to achieve
the goals. All of the agents’ actions are based on beliefs and are according to the
goal cases. To assign and compose roles for an agent, we should:
• Assign and compose roles for agent design;
• Assign roles to agents with design quality in mind, where cohesion, low cou-
pling, and minimum need for communication are essential;
• The goals form the expertise for the agents. Splitting and merging may be re-
quired.
The output of this activity is a GCB (Goals, Goal Cases, Collaborators, and Beliefs)
card (refer to Table 3) that is used to document agents . This card can directly be taken
to the implementation stages .
Table 3. Agent specification template
Agent Name:
Goal Goal Case Collaborator Belief
List all goals List all goal cases List all collaborators List all beliefs
4 Agent Analysis
Before proceeding, we present a conceptual model for an agent-based system that
focuses entirely on business problems [7]. Taking the InfoSleuth architecture [19] as a
reference, our model consists of six different layers that make the system more com-
partmentalized and modularized. Each layer provides a level of abstraction and certain
services to the layer above it, while hiding the implementation of its services from the
higher layer. Also, each layer passes both data and control information to its corre-
sponding neighbors. Figure 3 depicts such a model for gathering information for a life
cycle model.
Ontology Layer (ONL)
Authentication Layer
Interface Layer (IL)
Service Broker Layer (SBL )
Service Layer (SL)
(AL)
Organizational Layer (OL)
Organisational Data files
DB
Fig.3. Conceptual model of information gathering system
This conceptual model covers many areas. First, an organization requires the abil-
ity to accurately identify a user who is making requests. In our system, the user’s
identity is verified by checking a password typed in during login. The process that
verifies and records the user’s identity is called authentication. This process is de-
signed to employ an access-control-list that contains a single entry that is authorized
to grant capabilities for other layers. In actuality, agents in every layer in an agent-
based information gathering system have to get security clearance from the authenti-
cation layer before they can request services.
The ontology layer collectively maintains a knowledge base of the different termi-
nology and concepts that are employed over the whole organization. This layer thus
describes the language that will be used for specifying and translating requests for
info rmation.
The interface layer is used to predict the user’s intentions and to request services
that are provided by the remaining modules. This layer acts on behalf of users to relay
specifications and obtain results. The broker layer models and delegates the services
of the overall organization and then provides them to users via the interface layer. The
service layer is used to provide services, which differs from the organizational layer
that controls resources. The service layer represents and provides the high level ser-
vices that can be formed by encoding expertise and by utilizing the organizational
layer. The organizational layer can be used to manage the organizational resources.
Its main task is to gather data from the various sources.
We then need to examine role models to determine their relevance and applicability
to the conceptual model. We concentrate on the organizational layer here, and these
roles, which we term organizational roles, are responsible for controlling resources.
We considered the following role models: Master/Slave [1], Manager [22], Bodyguard
[18], and Adapter [8]. An organizational role can be another form of an Adapter, as it
can reformulate requests to the different databases. However, the organizational role
is also responsible for database activation and the supervision of any security restric-
tions. The activation behavior resembles that of a Manager, while the security super-
vision facets of this role resemble those found in the Bodyguard role model. When an
organizational role acts as a Bodyguard or an Adapter, the database is the Target and
the Subject. These roles are summarized in Table 4 [29].
Table 4. Organizational roles
Organization Role
Role type Responsibilities Collaborators
Manager (Man- • To manage the information resource role
ager)
Slave (Mas- • To receive the request from Master s ervice role
ter/Slave) • To perform a task and send the reply
to Mas ter
Client (Bodyguard) • To request the permis sion of a service authentication
role
Subject (Body- • To accept the notific ation of a service authentication
guard) role
Client (Adapter) • To send message to Adapter and ontology role
collaborate with the Target
Target (Adapter) • To receive the me s sage sent by Client ontology role
• To perform a task and send a reply
The roles in the other layers can be identified in a similar manner. However, in t he
following sections, we will only illustrate the application of our methodology for the
organizational layer.
5 Agent Design
5.1 Basic Requirements of the CASA Model
The CASA model estimates costs throughout all life cycle phases, including mainte-
nance. The maintenance of a product involves plans, labor, equipment, material, spare
parts, transportation, recurring facilities, recurring item management, software mainte-
nance, contractor services and engineering changes. The relevant information is
stored in organization databases. To estimate life cycle costs with the CASA model,
this information must be gathered. We will focus on cost estimation for maintenance
and give a brief description for a use case as below:
The user formulates a request for maintaining a product. When receiving the request,
the maintainer will ask a planner for a maintenance plan, including schedules and ac-
tions. He/she then asks an estimator to estimate costs for all components of the
product. To estimate costs, the estimator will gather the information required by the
CASA model from organizational databases. This information is related to plans, labor,
equipment, material, spare parts, and management, such as transportation, recurring
facilities, recurring item management, software maintenance, contractor services and
engineering changes. Particularly, if spare part information is not available in the or-
ganizational databases, the estimator will ask suppliers to provide it.
5.2 Goals, Goal Cases, and Beliefs.
We can identify the goals for information gathering from section 5.1. Figure 4 shows a
goal diagram that represents the goals of the system in hierarchical structure.
Obtain
Information
Obtain
Maintenance
Information
Obtain Obtain Plan Obtain Obtain Obtain Obtain
Product Requirements Labor Equipment Material Management
Fig. 4. Goals for searching for maintenance information
There are three levels in this figure. The top level contains “Obtain Information”
that is a general goal for obtaining information. The second level is “Obtain Mainte-
nance information”, and this is a goal for obtaining information required for mainte-
nance cost estimation. There are six goals in the third level. These goals can be used
to search information specific to the CASA model.
To illustrate how to develop a goal case, consider the “Obtain Material” goal as an
example. As we know, material information can be obtained from three data sources:
organizational databases, suppliers’ catalogues and user experiences. Therefore, the
“Obtain Material” goal can be achieved by using a goal case described below:
Obtain Material goal case (GC1)
Pre -condition:
The product information is available
Flow of events
Basic paths:
1. The goal case starts when the agent is requested to achieve the “Obtain Mat e-
rial” goal.
2. The agent attempts to achieve the “Manage Material” goal for information from
an organizational database.
3. If it cannot find data from organizational database, the agent attempts to achieve
the “Manage Supplier” goal.
4. If it cannot find data from suppliers or any error occurs, the agent asks user to
enter data by posting the “Manager Manual Entry” goal.
5. The agent replies to the requested agent with the material data.
Post-condition:
The agent stores the material information.
We can identify the belief “Material” from this goal case, which is the knowledge
used to describe or present the material information.
5.3 Roles and Goal Assignment
The organizational roles in Table 4 are responsible for gathering information that is
needed by algorithms in the CASA model. This information includes the product to be
maintained, the plan and requirements to be implemented, labor and equipment to be
used, materials to be consumed and spare parts that need to be purchased from su p-
pliers. In addition, CASA also requires management information involving transporta-
tion, recurring facilities, recurring item management, software maintenance, contractor
services and engineering changes . Therefore, it is practical that each individual agent
that is assigned a role is responsible for gathering information in its specialized field.
As a result, the organizational roles can be instantiated in our application as shown as
in Table 5. After that we assign the goals to roles by using RRC cards. However, this
procedure is not discussed in this paper.
Table 5. Instances of the organizational role
Composite Role Instantiated Roles Goal
Organizational Role Labour manager Obtain Labour
Equipment manager Obtain Equipment
Material manager Obtain Material
Manage Material
Manage Supplier
Manage Manual Entry
Management manager Obtain Management
Project manager Obtain Product
Plan & Requirement manager Obtain Plan & Requirement
5.4 Agent S pecification
According to Table 5 and the goals that we have identified, we assign and compose
roles to agents, as shown as in Table 6. It is worth mentioning that this approach
allows organizations to create their new assignments when organizational changes
occur.
Table 6. Organizational Agent identification
Composite Instantiated Roles Potential agents used
Roles
Organizational Project manager, Project agent
Role Plan & requirement ma nager
Labor manager Resource agent
Equipment manager
Material manager Inventory agent
Supplier manager.
Management ma nager Management agent
To document agents, consider the Inventory agent as an example. Table 7 shows a
GCB card for specifying this agent. The rest of agents can be documented in a similar
manner.
Table 7. Simplified Inventory Agent specification
Inventory Agent
Goal Goal Case Collaborator Belief
Obtain Material GC1 Estimator Mat erial
Manage Material GC2* Organization Database Mat erial
Manage Supplier GC3* Supplier Database Mat erial
Manage Manual Entry GC4* User Mat erial
* Goal cases are not specified in this paper
6 Experimental Evaluation
To realize our system, we used the JACK framework [4] that provides four main class-
level constructs: Agent, Database, Event, and Plan for a BDI agent. Note that the
Agent construct includes what type of messages and events an agent responds to,
and which plan it uses to achieve its goals. It not only has methods and data members
just like objects, but it also contains database relations that an agent can use to store
beliefs, descriptions of events that the agent can handle, and plans that the agent uses
to handle the events. Table 8 shows rules that can be used to map a GCB card to
JACK constructs
Table 8. Mapping from a GCB card to JACK agent
GCB card Goal Goal case Collaborator Belief
JACK agent constructs Event Plan Agent Database
By applying the above mapping, we have implemented our agents to gather infor-
mation for the CASA model. Figure 5 shows a screen shot from our system that is able
to estimate a life cycle cost for a computer system.
Fig. 5. Results
In this figure, the left side pane shows a tree structure that represents the product
of computer system in the form of assembly. For estimating cost for the whole product
or a subsystem, just click the manual. The right side pane lists the results, which d e-
scribes the event, plan and agent tasks in time order.
7 Summary
In this paper, we have presented a methodology of AOSE for identification of goals
and goal cases based on an organizational view and roles. We have applied this meth-
odology to an information gathering system for LCC. This methodology is a syste m-
atic approach that uses goals and roles. It generates results from the initial system
requirement to the implemented agent-based system. Furthermore, this methodology
can be applied to other agent-based information sys tems.
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