OBJECT-PROCESS METHODOLOGY APPLIED TO AGENT

DESIGN

Zoheir Ezziane

College of Information Technology, Dubai University College, P.O. Box 14143, Dubai, United Arab Emirates

Keywords: Object-process methodology, Agent design.

Abstract: As computer systems become ever more complex, we need more powerful abstractions and metaphors to

explain their operations. System development shows that designing and building agent systems is a difficult

task, which is associated with building traditional distributed, concurrent systems. Understanding natural,

artificial, and social systems requires a well-founded, yet intuitive methodology that is capable of modeling

these complexities in a coherent, straightforward manner. Object-Process Methodology (OPM) is a system

development and specification approach that combines the major system aspects (function, structure, and

behavior), into an integrated single model. This paper will provide a paradigm for designing agent systems

using the object-process methodology. It aims to identify design concepts, and to indicate how they interact

with each other.

1 INTRODUCTION

Systems and products are becoming increasingly

complicated. Technology has been so pervasive that

even commonly used products feature high

computational power, embedded within increasingly

miniature, precise, and involved hardware. Systems

of an infrastructure nature, such as the Internet and

digital economy, are orders of magnitude more

complex than products individuals normally use.

Understanding natural, artificial, and social

systems requires a well-founded, yet intuitive

methodology that is capable of modeling these

complexities in a coherent, straightforward manner.

Artificial systems require development and

support efforts throughout their entire lifecycle.

Systematic specification, analysis, design, and

implementation of new systems and products are

becoming even more challenging and demanding, as

contradicting requirements of shorter time-to-

market, rising quality, and lower cost, are on the

rise. These trends call for a comprehensive

methodology, capable of tackling the mounting

challenges that the evolution of new systems poses.

OPM is a system development and specification

approach that combines the major system aspects

(function, structure, and behavior), into an integrated

single model. This methodology is used to design

multiagent systems.

Multiagent systems (MAS) are concerned with

the behavior of a collection of possibly pre-existing

autonomous agents aiming at solving a given

problem. A MAS can be defined as a loosely

coupled network of problem solvers that work

together to solve problems that are beyond the

individual capabilities or knowledge of each solver

(Jennings et al., 1998). These problem solvers

(agents) are autonomous and may be heterogeneous

in nature.

This paper will provide a paradigm for designing

agent systems using the object-process

methodology. It aims to identify design concepts,

and to indicate how they interact with each other.

2 BACKGROUND

2.1 Object-Process Methodology

OPM takes a fresh look at modeling complex

systems that comprise humans, physical objects, and

information (Dori, 2002). OPM is a formal paradigm

to systems development, lifecycle support, and

evolution. OPM was applied in such diverse areas as

computer integrated manufacturing (Dori, 1996),

image understanding (Dori, 1996), modeling

research and development environments

(Meyersdorf et al., 1997), document analysis and

455

Ezziane Z. (2004).

OBJECT-PROCESS METHODOLOGY APPLIED TO AGENT DESIGN.

In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 455-462

DOI: 10.5220/0002657604550462

 SciTePress

recognition (Wenyin, 1998), algorithm specification

(Wenyin et al., 1999), software engineering (Tomlin

et al., 1998), modeling electronic commerce

transactions (Dori, 2001), and web applications

(Reinhartz-Berger et al., 2002).

Natural and artificial systems alike exhibit

three major aspects: function, structure, and

behavior. In the case of artificial systems, OPM

provides a framework for the entire system’s

lifecycle, from the early stages of requirement

elicitation and analysis, through further development

and deployment, all the way to termination and

initiation of a new generation (Dori, 2002).

OPM combines formal yet simple graphics

with natural language sentences to express the

function, structure, and behavior of systems in an

integrated, single model. Objects and processes are

the two main building blocks that OPM requires to

construct models. A third OPM entity is state, which

is a situation at which an object can be and therefore

a notch below object. Objects, processes, and states

are the only bricks involved in building systems. The

links connecting these three entities act as the mortar

that holds them together (Dori, 2002).

OPM is an integrated approach to the study and

the development of systems in general and

information systems in particular. The basic premise

of OPM is that objects and processes are two types

of equally important classes of things, which

together describe the structure, function, and

behavior of systems in a single framework. OPM

unifies the system specification, design, and

implementation within one frame of reference, using

a diagramming tool (Object-Process Diagram), and a

corresponding, English-like language (Object-

Process Language) (Reinhartz-Berger et al., 2002).

OPM has two scaling mechanisms:

unfolding/folding and zooming-in/zooming-out. The

unfolding/folding mechanism uses structural

relations for detailing/abstracting the structural parts

of a thing. For example, in figure 1, the process

Traveling is unfolded to expose its parts, processes

Destination Selecting and Budgeting Planning.

The zooming mechanism exposes/hides the

inner details of a thing within its frame. In figure 2,

the process Traveling is zoomed-in. This way OPM

facilitates focusing on a particular subset of things

(object and/or processes), elaborating on their details

by drilling into them to any desired level of detail.

ICEIS 2004 - SOFTWARE AGENTS AND INTERNET COMPUTING

456

Agent

Environment

Percepts Actions

Figure 3: Agent interacts with its environment

2.2 Multiagent Systems

MAS provide a paradigm for analyzing, designing,

and implementing software systems. The agent-

based view offers a powerful repertoire of tools that

have the potential to considerably improve the way

in which people design and implement many types

of applications. Agents are being used in an

increasingly wide variety of applications, ranging

from small applications such as personalized email

filters (Abushar et al., 2003) to large, complex

mission critical systems such as Internet trading

(Chavez et al., 1996), (Stone et al., 2001), (Chan,

2001), (Das, 2003) and air-traffic control

(Cammarata, 1983), and (Tomlin, 1998). Kasbah is

an example of Internet trading, in which it

establishes a virtual marketplace on the Web where

users create autonomous agents to buy and sell

goods on their behalf (Chavez et al., 1996). In

addition to providing solutions to meet real-world

needs, they demonstrate to be a useful technology.

An agent is a computer system that is situated

in some environment, and that is capable of

autonomous action in this environment in order to

meet its design objectives (see figure 3). An agent

has ability to perceive, reason, act, and

communicate. Furthermore, an agent has explicitly

knowledge and methods for operating on or drawing

inferences from its knowledge. The key problem

facing an agent is that of deciding which action it

should perform in order to best satisfy its design

objectives (Wooldridge, 2002)

Agents operate and exist in some environment,

which is both computational and physical. The

environment might be open or closed, and it might

or might not contain other agents. Although there are

situations where an agent can operate usefully by it

self, the increasing interconnection and networking

of computers is making such situations rare, and

therefore interaction among agents is the most

common situation (Weiss, 2000).

Much of the traditional AI has been concerned

with how an agent can be constructed to function

intelligently, with an internal reasoning and control.

However, intelligent systems do not function in

isolation. They are part of the environment in which

they operate, and the environment contains other

intelligent systems. Consequently, those systems

form a society of agents.

Information environments are too large,

complex, and open to be managed centrally.

Computational intelligence must be embedded in

such environments to provide distributed control. A

problem for a multiagent system is how it can

maintain global coherence without explicit global

control. Hence, agents must be adaptive (they should

be able to explore and learn their environment), and

social (they should interact and coordinate to

achieve their own goals, and the goals of their

society). One way of achieving coherence is through

social commitments.

The nature of the environment is highly

dependable on many properties. The environment

properties are classified as follows: Accessible

versus inaccessible; Deterministic versus non-

deterministic; Static versus dynamic; Discrete versus

continuous (Russel, 2003). The most complex class

of environments (i.e., open) is those that are

inaccessible, non-deterministic, dynamic, and

continuous. Environmental properties have a role in

determining the complexity of the agent design

process, but a second property such as the nature of

interaction between agent and environment is also

important to consider.

Details about how agent communicate,

cooperate, and negotiate are available in the

literature (Wenyin et al., 1998), (Wenyin et al.,

1999), (Weiss, 2000), (Russel, 2003), and (Jennings

et al., 1998).

2.3 Current Design Methods

Object-oriented (OO) design is a general paradigm

for developing systems that focuses on the objects

that build the system. The strength of OO techniques

is in modeling the structural aspects of a system.

OBJECT-PROCESS METHODOLOGY APPLIED TO AGENT DESIGN

457

However, they are far less suitable for representing

the dynamic and functional aspects of a system.

Current OO techniques suffer from three major

inter-related problems: the encapsulation, the

complexity management, and the model multiplicity

problem. They do not have a mechanism for

specifying stand-alone processes, which are not

owned by a certain object and counter the

encapsulation principle. Moreover, the encapsulation

principle eliminates the dynamic aspect of the

system. Thus, while being a useful programming

convention, this unnecessary encapsulation

constraint has been a source of endless confusion.

The complexity management problem is

related to the way OO methods deal with the

complexity that emerged by splitting systems into

various models such as structure (the object/class

model) and dynamics (Statecharts). Therefore, when

the complexity of the system increases, no tools will

be available to describe the entire system.

The inadequacy of accommodating the

functional and dynamics system aspects, which OO

methods suffer from, has contributed to the model

multiplicity problem (Peleg, 2000) and (Lovitz,

1998). The most common object-oriented modeling

language is UML (OMG, 2003), (Agarwal et al.,

2003), and (Medvidovic et al., 2002). The UML

standard requires nine different models, including

class diagrams, use case diagram, object message

diagram, state diagram, module diagram, and

platform diagram. The model multiplicity problem

refers to the need to comprehend a variety of models

of the same system and synchronize them. Hence,

the need to specify a system with just one model will

be definitely better than a multi-model one (Peleg,

2000).

3 AGENT SYSTEM DESIGN

USING OBJECT-PROCESS

METHODOLOGY

In this paper, we present an application of OPM to

modeling the basic multiagent design as a case to

demonstrate OPM’s semantics. However, OPM is

domain independent, and has been applied to many

areas. The feature of OPM is that it depends on how

objects and processes are defined. This characteristic

of OPM makes it suitable for developing systems in

a large variety of domains.

Viewing agents from an abstract level prepares

the ground for a smooth analysis. However, it does

not necessarily lead to their construction. This

abstraction is a useful software engineering

abstraction (e.g., abstract data types). Hence, OPM

is used to refine the model of agents, by breaking it

down into sub-systems in exactly the way that one

does in standard software engineering. As the view

of agents is being refined, design options related to

the agent’s subsystem would become transparent.

Figure 4 describes the OPM design for a multiagent

system, indicating the characteristics of an agent and

the way it interacts with its environment.

ICEIS 2004 - SOFTWARE AGENTS AND INTERNET COMPUTING

458

Mobility indicates whether the agent has any ability

to move from one site to another. One of the

characteristics of social agents is that participating

agents ought to have some sort of rationality, in

order to build a coherent society. Adaptation means

whether the agent should adapt itself to how the

environment evolves

3.1 Simple Reflex Agents

One of the simplest classes of agents is the simple

reflex agent, also called purely reactive agents. They

simply respond directly to their environment. These

agents select actions based on the current percept

ignoring the rest of their percept history. They base

their decision-making entirely on the present.

A lamp agent is an example of a simple reflex

agent. Assume that the lamp’s environment can be in

one of the two states (either on, or off). Figure 5

depicts this kind of agents.

3.2 State-based Reflex Agents

The model for simple reflex agents helps designing

agents with state. That is, the agent should keep

track of an internal state that depends on the percept

history. These agents must have some internal data

structure, which is used to update the internal state

information. Thus, the internal state of any agent

needs to record how the world evolves

independently of the agent, and how the agent’s

actions do affect the environment. Figure 6 depicts

an OPM design for sate-based reflex agents.

OBJECT-PROCESS METHODOLOGY APPLIED TO AGENT DESIGN

459

A good example of purely reactive agents is an

internet personal assistant, which can be used to help

the user by storing the preferred links, advice the

user to visit a certain site based on the user’s profile,

etc.

3.3 Utility-based Agents

A utility function is needed in order to map a state

(or a sequence of states) onto a real number, which

describes the associated degree of happiness. This

utility function is used to specify the appropriate

tradeoff that could result from certain action. Figure

7 depicts an OPM design for utility-based agents.

Game assistant is an example of a utility-based

agent. It suggests to the user to make certain moves,

using a certain strategy that either minimize

penalties or increase points.

3.4 Negotiations and Reaching

Agreements

Cooperation is a concept in the human world. Often

when a group of people work together to solve a

problem or to achieve an objective, not only they

can increase productivity and efficiency, but also

they can solve a problem that cannot be solved by an

individual alone.

Communication can enable the agents to

coordinate their actions and behavior, which results

in a more coherent society. The desired property of

coordination avoids resource contention, livelock,

and deadlock. Typically, in order to reach a

successful cooperation, each agent must maintain a

kind of a model of how other agents evolve in the

environment.

As electronic commerce (e-commerce) is

rapidly becoming a reality, the need for negotiating

methods that take into consideration the

complexities of the real world environment, such as

incomplete data and negotiation deadline.

A frequent form of interaction that occurs

among agents with different goals is termed

negotiation. A negotiation is a process by which a

joint decision is reached by two or more agents,

where each one is trying to reach an individual goal.

In this case, the agents need to communicate their

actual positions, which might conflict, and then try

to make an agreement through concessions or

searching for other options.

One of the most important issues in both

conventional and electronic trading is for sellers and

buyers to reach a consensus on pricing and other

terms of transactions. The task of negotiation and

reaching an agreement can often be difficult and

time consuming. Thus, it is crucial to use a

transparent design tool to design and engineer a

society of trading agents that help users negotiate

terms of business transactions.

3.4.1 Auctions

Recently, online auctions rapidly achieved enormous

popularity in e-commerce. Numerous online auction

houses have been already established on the web,

such as Amazon, BargainFinder and eBay. Hence,

the design and development of a multiagent system

will provide some opportunities to improve auction

performance.

Agents can be used to compare products of

several producers and help to find the best bargain,

ICEIS 2004 - SOFTWARE AGENTS AND INTERNET COMPUTING

460

but they can also be used to perform the actual

transaction. The interest is focused on the latter case

because it represents an actual multiagent e-

commerce system. If a direct trading procedure is

followed, agents of producers and consumers should

negotiate the deal and inform their users. In case of a

very simple negotiation, the transaction can be done

without the use of an institution. For example, an

agent can buy a book at Amazon.com, without

requiring an institution because the transaction is

simple.

However, the transaction can be performed in

other cases where a certain institution is needed. The

institution determines the way that the parties can

conduct the transaction and provides an

infrastructure to do it. A good example is Kasbah, a

market place where agents can buy and sell goods.

Kasbah gives a predefined structure for specifying

the product and also facilitates the negotiation

process. Kasbah most closely resembles an online

classified ad system. When a user wants to sell

something (e.g., used book) he/she registers the

object for sale with the Kasbah server via a

computer interface, buyers then go to Kasbah to look

for something to purchase. But Kasbah is able to use

software agents to negotiate a sale.

In the case of indirect trading, there exists a

natural institution (e.g., auction) in the form of the

broker that mediates in the transaction. The auction

determines the rules of encounters, such as how

products can be delivered at auction level as well as

how products can be bought. Despite the auction’s

simplicity, they present a powerful tool that

automated agents can use for allocating goods, tasks,

and resources.

An auction takes place between an agent

(auctioneer) and a set of agents (bidders). The

objective of the auction is for the auctioneer to

allocate the good to one of the bidders. Usually, the

auctioneer would like to maximize the price at

which the good is allocated, while the bidders want

to minimize it. Achieving the auctioneer’s goal is

realized through some rules of encounter. Figure 8

shows an attempt to design an auction multiagent

system.

Winner determination is an auction protocol.

The first-price is used to allocate the good to the

agent that bids the most, while the second-price is to

allocate the good to the second highest bid. The

other auction protocol specifies on how bids are

announced. If the bids are known and made

available to all agents in the system, then the auction

is open-cry, otherwise

it is sealed-bid.

4 CONCLUSION

This paper emphasizes on a different framework

(OPM) in designing agents. OPM is able to represent

all the important interaction in a system, and is

widely used in various fields. This approach

includes a clear and concise set of symbols that form

a language enabling the expression of the system’s

building blocks and how they relate to each other.

Various attempts in designing agent systems were

approached; a comparison of OPM with UML was

discussed. It was shown how OPM is more effective

than the current UML. As an extension to this

research, it would be interesting to investigate the

compliance of OPM in designing agents with FIPA

standards. Many major IT and telecommunications

companies had become involved in the FIPA trend,

and a set of prototypical standards had been

developed. Hence a new methodology in designing

agents such as the one discussed here in this paper

will be more valuable when is compliant with FIPA

specifications.

Future work could also emphasize on a detailed

OBJECT-PROCESS METHODOLOGY APPLIED TO AGENT DESIGN

461

description of the transactional process that occurs

during trading. Another approach would be to

investigate how bidding agents buy and sell multiple

interacting goods in auctions of different types,

which represent complex and rapidly advancing

domains of e-commerce and e-marketing.

REFERENCES

Abushar, S. and Hirata, N. Filtering with Intelligent

Software Agents. Available from:

<http://www.engin.umd.umich.edu/CIS/course.des/cis

479/projects/FISA.html.> [Accessed September 10,

2003]

Agarwal, R. and Sinha, A. P., 2003. Object-Oriented

Modeling with UML: A Study of Developer’s

Perceptions. Communication of the ACM, 46(9), 248-

256.

Cammarata, S., McArthur, D., and Steeb, R., 1983.

Strategies of Cooperation in Distributed Problem

Solving, Proc. 8

Int’l Joint Conf. on AI (IJAI-83),

Elsevier, Karlsruhe, Germany, 767-770.

Chan, T., 2001. Artificial Markets and Intelligent Agents,

Ph.D. dissertation, Dept. of Electrical Eng. and

Computer Science, Massachusetts Institute of

Technology, Cambridge, Mass., USA.

Chavez, A. and Maes, P., 1996. Kasbah: An agent

marketplace for buying and selling goods. Proc. 1

Int’l Conf. on the Practical Application of Intelligent

Agents and Multi-Agent Technology, Practical

Application Company, London, UK, 75-90.

Das, S., 2003. Intelligent Market-Making in Artificial

Financial Markets, MSc thesis, Dept. of Electrical

Eng. and Computer Science, Massachusetts Institute

of Technology, Cambridge, Mass., USA.

Dori, D., 1996. Object-Process Analysis of Computer

Integrated Manufacturing Documentation and

Inspection Functions. International Journal of

Computer Integrated Manufacturing, 9(5), 339-353.

Dori, D., 1996. Analysis and Representation of the Image

Understanding Environment Using the Object-Process

Methodology, Journal of Object-Oriented

Programming, 9(4), 30-38.

Dori, D., 2001. Object-Process Methodology Applied to

Modeling Credit Card Transactions, Journal of

Database Management, 12(1), 2-12.

Dori, D., 2002. Object-Process Methodology

−

A Holistic

Systems Paradigm, New York: Springer.

Jennings, N.R. Sycara, K. and Wooldridge, M. (1998). A

Roadmap of Agent Research and Development,

Autonomous Agents and Multi-Agent Systems, 1, 275-

306.

Kovitz, B.L., 1998. Practical Software Requirements: A

Manual of Content and Style, Manning Publication

Company.

Medvidovic, N., Rosenblum, D.S., Redmiles, D.F. and

Robbins,J.E., 2002. Modeling software architectures

in the Unified Modeling Language, ACM Transactions

on Software Engineering and Methodology.

(TOSEM), 11(1), 2-57.

Meyersdorf, D. and Dori, D., 1997. The R&D Universe

and Its Feedback Cycles: An Object-Process Analysis,

R&D Management, 27(4), 333-344.

OMG, Unified Modeling Language Specification.

Available from:

<http://www.omg.org/technology/documents/formal/u

ml.htm> [Accessed September 9, 2003]

Peleg, M. and Dori, D., 2000. The model multiplicity

problem: Experimenting with real-time specification

methods, IEEE Transaction on Software Engineering,

26(8), 742- 759

Reinhartz-Berger, I. and Dori, D., 2002. OPM/Web−

Object-Process Methodology for Developing Web

Applications, Annals of Software Engineering, 13,

141-161.

Russel, S. and Norvig, P., 2003. Artificial Intelligence: A

Modern Approach, 2

ed., New Jersey: Prentice-Hall,

USA.

Stone, P. and Greenwald, A., 2001. Autonomous bidding

agents in the trading agent competition. IEEE Internet

Computing, 5(2), 52-60.

Tomlin, C., Pappas, G. J. and Sastry, S., 1998. Conflict

resolution for air traffic management: A study in

multi-agent hybrid systems, IEEE Transaction on

Automatic Control, 43, 509-521.

Weiss, G., 2000. Multiagent Systems: A Modern Approach

to Distributed Artificial Intelligence, MIT Press,

Cambridge, Mass. USA.

Wenyin, L. and Dori, D., 1998. A Generic Integrated Line

Detection Algorithm and its Object-Process

Specification, Computer Vision

−

Image

Understanding, 70(3), 420-437.

Wenyin, L. and Dori, D., 1999. Object-Process Diagrams

as an Explicit Algorithm Specification Tool, Journal

of Object-Oriented Programming, 12(2), 52-59.

Wooldridge, M., 2002. An Introduction to MuliAgent

Systems, John Wiley & Sons, UK.

ICEIS 2004 - SOFTWARE AGENTS AND INTERNET COMPUTING

462