A DYNAMIC INFERENCE MODEL FOR INTELLIGENT AGENTS

This paper proposes an Agent Inference Model (AIM) for constructing intelligent software agents. AIM has the ability of representing various types of fuzzy concepts, temporal concepts, and dynamic causal relationships between concepts. It also has the ability of handling feedback and analyzing inference patterns over different causal impact models. Based on AIM, a new type of intelligent agent, Dynamic Inference Agent (DIA) is presented. A dynamic inference agent has the ability to model, infer and make decisions on behalf of human beings. It uses numeric representations and computation instead of symbolic representation and logic deduction to represent knowledge and to carry out the inferences respectively. Thus the construction of DIA is simplified and the implementation code is compact. The application of DIA to various areas, especially for electronic commerce over the Internet is exemplified.

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Intelligent Software Agents in Electronic Commerce

Human Computer Interaction ◽

10.4018/978-1-878289-91-9.ch006 ◽

2011 ◽

pp. 104-112 ◽

Cited By ~ 1

Author(s):

Mahesh S. Raisinghani ◽

Christopher Klassen ◽

Lawrence L. Schkade

Keyword(s):

Intelligent Agents ◽

Software Agents ◽

Intelligent Agent ◽

Software Agent ◽

Reasoning Strategies ◽

Intelligent Software ◽

Collaborative Behavior ◽

Intelligent Software Agents ◽

The Status ◽

High Level

Although there is no firm consensus on what constitutes an intelligent agent (or software agent), an intelligent agent, when a new task is delegated by the user, should determine precisely what its goal is, evaluate how the goal can be reached in an effective manner, and perform the necessary actions by learning from past experience and responding to unforeseen situations with its adaptive, self-starting, and temporal continuous reasoning strategies. It needs to be not only cooperative and mobile in order to perform its tasks by interacting with other agents but also reactive and autonomous to sense the status quo and act independently to make progress towards its goals (Baek et al., 1999; Wang, 1999). Software agents are goal-directed and possess abilities such as autonomy, collaborative behavior, and inferential capability. Intelligent agents can take different forms, but an intelligent agent can initiate and make decisions without human intervention and have the capability to infer appropriate high-level goals from user actions and requests and take actions to achieve these goals (Huang, 1999; Nardi et al., 1998; Wang, 1999). The intelligent software agent is a computational entity than can adapt to the environment, making it capable of interacting with other agents and transporting itself across different systems in a network.

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Software Agents in Today's Digital Economy

Internet Commerce and Software Agents ◽

10.4018/978-1-930708-01-3.ch006 ◽

2001 ◽

pp. 88-100 ◽

Cited By ~ 2

Author(s):

Mahesh S. Raisinghani

Keyword(s):

Intelligent Agents ◽

New Technology ◽

Software Agents ◽

Information Filtering ◽

Intelligent Agent ◽

Social Dimension ◽

Economic Changes ◽

Software Products ◽

Intelligent Software Agents ◽

High Level

One of the most discussed topics in the information systems literature today is software agent/intelligent agent technology. Software agents are high-level software abstractions with inherent capabilities for communication, decision making, control, and autonomy. They are programs that perform functions such as information gathering, information filtering, or mediation (running in the background) on behalf of a person or entity. They have several aliases such as agents, bots, chatterbots, databots, intellibots, and intelligent software agents/robots. They provide a powerful mechanism to address complex software engineering problems such as abstraction, encapsulation, modularity, reusability, concurrency, and distributed operations. Much research has been devoted to this topic, and more and more new software products billed as having intelligent agent functionality are being introduced on the market every day. The research that is being done, however, does not wholeheartedly endorse this trend. The current research into intelligent agent software technology can be divided into two main areas: technological and social. The latter area is particularly important since, in the excitement of new and emergent technology, people often forget to examine what impact the new technology will have on people’s lives. In fact, the social dimension of all technology is the driving force and most important consideration of technology itself. This chapter presents a socio-technical perspective on intelligent agents and proposes a framework based on the data lifecycle and knowledge discovery using intelligent agents. One of the key ideas of this chapter is best stated by Peter F. Drucker in Management Challenges for the 21st Century when he suggests that in this period of profound social and economic changes, managers should focus on the meaning of information, not the technology that collects it.

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Intelligent Software Agents

Information Security Management ◽

10.4018/978-1-878289-78-0.ch011 ◽

2011 ◽

pp. 155-172

Author(s):

Stefan Kirn ◽

Mathias Petsch ◽

Brian Lees

Keyword(s):

Intelligent Agents ◽

New Technology ◽

Software Agents ◽

Security Architecture ◽

Multi Agent Systems ◽

Agent Systems ◽

Security Issues ◽

Intelligent Software ◽

Intelligent Software Agents ◽

Multi Agent

For a new technology, such as that offered by intelligent agents, to be successful and widely accepted, it is necessary for systems, based on that technology, to be capable of maintaining security and consistency of operation when integrated into the existing infrastructure of an organisation. This paper explores some of the security issues relating to application of intelligent agents and the integration of such systems into existing organisations. First, existing information security issues for enterprises are considered. Then, a short introduction to the new technology of agents and agent systems is given. Following this, the special security problems of the new technology of software agents and the emerging risks for software and enterprises are discussed. Finally, a new security architecture for multi-agent systems is proposed, together with an explanation of how this multilevel architecture can help to improve the security of agent systems.

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APPLYING INTELLIGENT SOFTWARE AGENTS IN A DISTRIBUTED CHANNEL ALLOCATION SCHEME FOR CELLULAR NETWORKS

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001402002131 ◽

2002 ◽

Vol 16 (08) ◽

pp. 1021-1039

Author(s):

ELIANE L. BODANESE ◽

LAURIE G. CUTHBERT

Keyword(s):

Mobile Communications ◽

Intelligent Agents ◽

Channel Allocation ◽

Base Stations ◽

Agent Negotiation ◽

Intelligent Software ◽

Intelligent Software Agents ◽

Distributed Channel Allocation ◽

Limited Spectrum

As the demand for mobile services has increased, the need for an efficient allocation of channels is essential to ensure good performance, given the limited spectrum available. Techniques for increasing flexibility in radio resource acquisition are needed to handle the heterogeneity of services and bit rates to be supported in the forthcoming generations of mobile communications. To improve the performance and efficiency of the channel allocation, we propose the use of a particular agent architecture that allows base stations to be more flexible and intelligent, including planning to attempt to balance the load in advance of reactive requests. The simulation results prove that the use of intelligent agents controlling the allocation of channels is feasible and the agent negotiation is an important feature of the system in order to improve perceived quality of service and to improve the load balancing of the traffic.

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ENABLING INTELLIGENT AGENTS AND THE SEMANTIC WEB FOR E-COMMERCE

International Journal of Computing ◽

10.47839/ijc.2.3.246 ◽

2014 ◽

pp. 153-159

Author(s):

Yefim H. Kats

Keyword(s):

Semantic Web ◽

Intelligent Agents ◽

Intelligent Agent ◽

Intelligent Software

This paper examines an impact of the growing intelligent agent technologies and the Semantic Web on the phenomenon of e-commerce. We discuss the problems – technical as well as legal – arising from the emergence of the new forms of intelligent software and consider the possible solutions. In particular, we review how the integration of the Semantic Web and intelligent agents can provide a new environment for the secure and scalable e-commerce applications.

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Intelligent agents, Internet information and interface

Australasian Journal of Educational Technology ◽

10.14742/ajet.2077 ◽

1995 ◽

Vol 11 (2) ◽

Author(s):

James Meek

Keyword(s):

Intelligent Agents ◽

Software Agents ◽

Intelligent Software ◽

Intelligent Software Agents ◽

Internet Information

<span>A review of intelligent software agents and their relevance to networked information touching on some of their emerging potential and on interface considerations.</span>

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Intelligent Software Agents in E-Commerce

Encyclopedia of Information Science and Technology, Second Edition ◽

10.4018/978-1-60566-026-4.ch336 ◽

2011 ◽

pp. 2137-2140

Author(s):

Mahesh S. Raisinghani ◽

Christopher Klassen ◽

Lawrence L. Schkade

Keyword(s):

Intelligent Agents ◽

New Technology ◽

Intelligent Agent ◽

Social Dimension ◽

Agent Technology ◽

Science Literature ◽

Software Technology ◽

Software Products ◽

Intelligent Software Agents ◽

The Social

Agent technology is one of the most widely discussed topics in information systems and computer science literature. New software products are being introduced each day. A growing number of computer information professionals recognize that there are definite issues surrounding intelligent agent terminology. These must be resolved if agent technology is to continue to develop and establish. Current research on intelligent agent software technology can be categorized as two main areas: technological and social. In the excitement of emergent technology, people often forget to scrutinize how new technology may impact their lives. The social dimension of technological progress is the driving force and most central concern of technology. Technology is not created for its own sake as a technological imperative. This article critiques the current state of software intelligent agents by examining technological issues and the social implications of intelligent agent software technology.

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Book Review: E-learning with Intelligent Agents [review of "Designing Distributed Learning Environments with Intelligent Software Agents" (Lin, F.O., ed.; 2004)]

IEEE Distributed Systems Online ◽

10.1109/mdso.2008.6 ◽

2008 ◽

Vol 9 (2) ◽

pp. 4-4 ◽

Cited By ~ 3

Author(s):

Maria Ganzha

Keyword(s):

Learning Environments ◽

Intelligent Agents ◽

Software Agents ◽

Distributed Learning ◽

Intelligent Software ◽

Distributed Learning Environments ◽

Intelligent Software Agents ◽

E Learning

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Intelligent Software Agents in E-Commerce

Electronic Commerce ◽

10.4018/978-1-59904-943-4.ch045 ◽

2011 ◽

pp. 549-554

Author(s):

Mahesh S. Raisinghani ◽

Christopher Klassen ◽

Lawrence L. Schkade

Keyword(s):

Information Systems ◽

Computer Science ◽

Software Agents ◽

Intelligent Agent ◽

Agent Technology ◽

Science Literature ◽

Software Products ◽

Intelligent Software ◽

Intelligent Software Agents ◽

Information Professionals

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Adaptive Tutoring System With Application of Intelligent Agents

International Journal of Smart Education and Urban Society ◽

10.4018/ijseus.2018040101 ◽

2018 ◽

Vol 9 (2) ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Jaroslav Meleško ◽

Eugenijus Kurilovas

Keyword(s):

Systematic Review ◽

Data Mining ◽

Learning Styles ◽

Intelligent Agents ◽

Web Of Science ◽

Learning System ◽

Tutoring System ◽

Personalised Learning ◽

Intelligent Software ◽

Intelligent Software Agents

In this article, the authors suggest a methodology to adapt learning units to the needs and talents of individual students using an intelligent learning system. Learning personalisation is done based on several factors. Felder and Silverman Learning Styles model is used to create student's profile with conjunction of data mining technologies and previously recorded behaviour of the student. Firstly, the authors perform systematic review of application of intelligent software agents in teaching throughout Clarivate Analytics Web of Science database. Secondly, they present methodologies to personalise learning by means of intelligent technologies. They analyse preferences of students according to Soloman-Felder Learning Styles questionnaire. The resulting model of a student is used in the creation of a personalised learning unit. The model of an adaptive intelligent teaching system based on application of aforementioned technologies is presented in more detail.

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