Agents are the new defacto standard for inclusion in modules of today’s software systems such as ERP systems, mobile applications and operating systems. Agents are an integral part of today’s software design. The question is how do intelligent agents work in the specific area of ERP credit card processing e-commerce models?
By Anne T. Galante
Department of Computing Systems, SUNY Farmingdale
State College, Farmingdale, USA
Abstract:
Agents are the
new defacto standard for inclusion in modules of today’s software systems such
as ERP systems, mobile applications and operating systems. Agents are an
integral part of today’s software design. The question is how do intelligent
agents work in the specific area of ERP credit card processing e-commerce
models? To answer this question, a specific area of ERP systems will be
analyzed: credit card processing for merchants. One specific merchant credit
card processor will be specifically investigated: EVO Merchants. This paper
will research how exactly does ERP systems interact using Application
Programing Interface or “API” specified by a credit card clearing house. Secure
Socket Layers or SSL, and XML are discussed and elaborated on specifically how
intelligent agents play such a pivotal role in ERP e-commerce systems for
credit card processing.
Keywords:
Intelligent
Agents Agent Technologies, ERP, E-Commerce, API, Artificial Intelligence,
Decision Making, Enterprise Resource Planning, Application Programing
Interface, EVO, Credit Cards, XML, SSL, AIM, W3C, SGML
1. Introduction
A General Learning Agent / Source : Wikimedia Commons
Agent
technologies are the foundation of software applications that are used in
everyday life. Agents are quite flexible and because of this specific attribute,
it gives them a particular strength. While agents have a particular quiet and
unobtrusive characteristic, they play a crucial hand running in the background
or performing automotive activities for specific users. Agents are essential to
the current Enterprise Resource Planning or ERP systems specifically in today’s
environment where the use of credit cards payments is so prevalent.
According to the
2013 Federal Reserve Payment Study, the number of noncash payments (credit, and
debit cards excluding electronic funds transfers or “EFT”) in 2012 reached a
whopping 122.8 billion of transactions, with a value of $79.0 trillion dollars
[1]
. For ERP systems, agent technologies play a pivotal role in the processing of
credit and debit cards.
MIT research
first introduced the concept of agent technology in 1961. Agent technologies
are found in a vast scope of applications ranging in the field of information
sciences, computer science, specifically artificial intelligence, and other
diverse traditional areas of science. According to Jennings and Wooldridge [2]
, an agent is located in a particularly complex environment that is an
encapsulated system. An agent’s primary focus is its main design objective and
because of this, is goal driven and often at times collaborative.
Agents are the
new defacto standard for inclusion in modules of today’s software systems such
as ERP systems, mobile applications and operating systems. Agents are an
integral part of today’s software design. The question is what advantages to
agents give to a software engineer? To answer this question a specific area of
ERP systems will be analyzed: credit card processing for merchants.
Agents are
helpful because they can perform a variety of tasks, and thus are very
flexible. According to Rusbridge [3]
, agents are responsible for the functions of observation, recognition,
planning and or inference and action or execution. Agents are customized for
application in complex systems, such as ERP systems. Some of the tasks agents
accomplish are the ability to translate, communicate and publish information.
Agents also can guide the user’s search query from the interface to the
appropriate target. Agents also negotiate, and gain access to exchange
information with other agents. This negotiation is similar to a conversation
that allows agents to determine which tasks are performed within the context of
pre-determined tasks. These specialized features allow for the effective
management of the user’s environment. Agents are systems that facilitate
different areas in ERP systems, such as Supply Chain or “SC” and Customer
Relationship Management or “CRM” are can be viewed as a network of autonomous
and collaborative units that regulate, control and organize all activities contained
in the ERP system [4] .
Prior research
on intelligent agent system architectures has shown that problems exist within
highly distributed systems that require synergy of many elements; a solution is
multi-agent systems or “MAS” [5]
[6]
. Additional research investigated intelligent agents and ERP scheduling
systems for taxi companies. The conclusion was that agents are a palpable
solution for a large taxi company’s complex scheduling system in London. This
solution gave the ability to schedule a taxi and provide the form of payment by
credit card, but it did not go into detail how the agents were involved in the
credit card processing in the ERP systems [7]
. Exactly how the agent works with credit card processing merchants, the ERP
software is the focus of this research.
The intelligent
agent plays a pivotal role in the function of using a credit card in an ERP
system to pay for goods. The example of a customer ordering goods from an
e-commerce site from a specific merchant, where the ERP order application would
then call an Application Programing Interface or “API” specified by a credit
card clearing house. The API will process the customer’s credit or debit card
information. An example of a company who provides these types of specifications
for API for credit cards is EVO Payments International. EVO founded in 1989,
and EVO’s corporate office is located in New York. EVO Payments International
is among the largest fully integrated merchant acquirer and payment processors
in the world. EVO operates as a payment service provider for both face-to-face
and e-commerce transactions for all major credit cards, debit cards, commercial
cards and electronic bank transfers. EVO can process in nearly 50 markets and
120 currencies around the world. Through its European subsidiary, EVO operates
as a principal member of MasterCard Worldwide and Visa Europe [8]
.
2. Problem
Definition
Some of the
specific tasks of the EVO credit card system, related to intelligent agents,
will be subsequently addressed and discussed. How does agent technology
interface with ERP systems and credit card processing merchants? These
questions are discussed in the research.
3. Advanced
Integration Method―AIM
The API tool EVO
provides is a merchant web services API, Advanced Integration Method or “AIM”.
AIM provides the necessary protocols to connect an e-commerce site or a retail
point of sale to the Authorize Net Payment Gateway to submit credit card
information by activating the AIM API. AIM will validate credit card
information, provide a receipt of the transaction, and will secure all credit
card information using a 128 bit Secure Socket Layer or “SSL”. According to
Bhiogade [9] SSL is a protocol developed by Netscape that establishes
a secure communication for a web browser and a web server. The SSL protocol
requires the web server to have a digital certificate installed in order for
the SSL connection to be created. SSL works by using a public key to encrypt
the data transferred over the SSL. Figure
1 shows the process on how to implement AIM.

The parsed
credit card information is embedded in an Extensible Markup Language or “XML”
document. According to Shanmugasundaram, Tufte, Zhang, He, DeWitt, &
Naughton [10] , XML is a class of data objects called XML documents
and partially and subsequently describes the behavior of the computer programs
that are responsible for processing the documents. XML is profile of Standard
Generalized Markup Language or “SGML” defined by the World Wide Web Consortium
or W3C. The World Wide Web Consortium or “W3C” is an international community
run by member organizations, a full-time staff, and the public. The members
work together to develop Web standards. Led by Web inventor Tim Berners-Lee and
CEO Jeffrey Jaffe, W3C’s mission is to lead the Web to its full potential [11]
. XML documents contained parsed data and are made up of character data called
entities. Markup is the encoded description of the documents storage layout and
logical structure. Figure 2 is an example of test data parsed and encoded in
XML.
Figure
2 shows the parsed data collected from the program developed by the
software engineer for the purpose of credit card processing. Each piece of data
is put into a markup, or encoded, with beginning and ending tags. This is
similar to the Hyper Text Markup Language or “HTML” works. For example, the
beginning structure is . The merchant name is parsed with and an ending tag.
The actual merchant name is placed in the markup.
4. Conclusions
AIM is an API
provided by EVO for merchants to install in ERP systems. There are four steps
to credit card approval (parse data, secure SSL, send data, receive a
response). Intelligent agents are the workers of the corporate ERP systems,
transferring data and performing services established in the software framework.
In order to implement AIM, a software developer must write a program or modify
and already established system to obtain the necessary credit card information.
A SSL certificate is installed on the server. The data is parsed with XML
according the vendor’s specifications. The data is transmitted to the payment
gateway and the AIM API goes to work. AIM validates the data, processes it, and
sends results back to the receiver.
The paper
researched how the implementation of the AIM API for merchant credit card
processing is seamless, transparent, and unobtrusive to users. The use of agent
technology gives maximum control over the merchants credit card processing. The
implementation of agents in complex environments is a palpable solution for
merchants with e-commerce sites. This solution is innovative, scalable, and
compatible with today’s modern trends for optimization and effectiveness.
About The Author:
Anne T. Galante , Department of Computing Systems, SUNY Farmingdale
State College, Farmingdale, USA
Cite this paper
Anne T.Galante,
(2015) Intelligent Agent Technologies: The Work Horse of ERP E-Commerce. International
Journal of Intelligence Science,05,173-176. doi: 10.4236/ijis.2015.54015
Copyright © 2015 by author and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
References
- 1. (2013) Federal Reserve Bank Service.
- 2. Wooldridge, M. and Jennings, N.R. (1995) Intelligent Agents: Theory and Practice. The Knowledge Engineering Review, 10, 115-152. http://dx.doi.org/10.1017/S0269888900008122
- 3. Rusbridge, C. (1998) Towards the Hybrid Library.
- 4. Symeonidis, A.L., Kehagias, D.D. and Mitkas, P.A. (2003) Intelligent Policy Recommendations on Enterprise Resource Planning by the Use of Agent Technology and Data Mining Techniques. Expert Systems with Applications, 25, 589-560. http://dx.doi.org/10.1016/S0957-4174(03)00099-X
- 5. Jennings, N.R., Sycara, K. and Wooldridge, M.J. (1998) A Roadmap of Agent Research and Devel-opment. Autonomous Agents and Multi-Agent Systems. Kluwer Academic, Boston.
- 6. Ferber, J. (1999) Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison Wesley Longman, Harlow.
- 7. Glaschenko, A., Ivaschenko, A., Rzevski, G. and Skobelev, P. (2009) Multi-Agent Real Time Scheduling System for Taxi Companies. Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems, Budapest, 10-15 May 2009, 29-36.
- 8. (2015) EVO Payments International. http://evopayments.com/about-evo/evo-payments-international
- 9. Bhiogade, M.S. (2001) Secure Socket Layer. Proceedings of the Computer Science and Information Technology Education Conference, Cork, June 2002, 85-90. http://dx.doi.org/10.2139/ssrn.291499
- 10. Shanmugasundaram, J., Tufte, K., Zhang, C., He, G., DeWitt, D.J. and Naughton, J.F. (1999) Relational Databases for for Querying XML Documents: Limitations and Opportunities. Proceedings of the 25th International Conference on Very Large Data, 1999, 302-314.
- 11. (2015) About W3C. http://www.w3.org/Consortium/
_______________________________________________________________________________
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