AN INTELLIGENT AGENT FOR WEB ADVERTISEMENTS

The rapid growth of Internet users attracts advertisers to post their advertisements in Internet. The probabilistic selection algorithm was not satisfactory; while other advertising agents are unable to guarantee the quality due to insufficient and unstable user information. This paper describes a new advertising agent based on user information. The users' interests are discovered by the Order Pattern Mining algorithm first, then the Gaussian curve transformation is applied to represent their profiles. For the advertisements, we use the keywords from different categories to construct the advertisement profiles as Gaussian curves also. This allows us to select advertisements based on the intersections of the different profiles according to users' preferences in an effective and efficient mechanism. A prototype of the Intelligent Advertising Agent has been developed with Java and Oracle. From our evaluations, we observed that about 70% of the test cases are successful in making predictions which generated the most favorable category that the users are interested.

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An Intelligent Agent based Incremental Feature Selection Algorithm for Effective Intrusion Detection in MANET

Asian Journal of Research in Social Sciences and Humanities ◽

10.5958/2249-7315.2017.00324.0 ◽

2017 ◽

Vol 7 (5) ◽

pp. 386

Author(s):

V Brindha Devi ◽

K L Shunmuganathan

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Intelligent Agent ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Agent Based ◽

Incremental Feature Selection

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RAKING: An Efficient K-Maximal Frequent Pattern Mining Algorithm on Uncertain Graph Database

Chinese Journal of Computers ◽

10.3724/sp.j.1016.2010.01387 ◽

2010 ◽

Vol 33 (8) ◽

pp. 1387-1395 ◽

Cited By ~ 4

Author(s):

Meng HAN ◽

Wei ZHANG ◽

Jian-Zhong LI

Keyword(s):

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

Graph Database ◽

Uncertain Graph ◽

Mining Algorithm ◽

Maximal Frequent Pattern

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Intelligent Agent Feasibility Study. Volume 1: Agent-based System Technology

10.21236/ada359612 ◽

1998 ◽

Author(s):

Stephen E. Deutsch

Keyword(s):

Feasibility Study ◽

Intelligent Agent ◽

System Technology ◽

Agent Based

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Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

Mathematics ◽

10.3390/math9060681 ◽

2021 ◽

Vol 9 (6) ◽

pp. 681

Author(s):

László Barna Iantovics

Keyword(s):

Problem Solving ◽

Scientific Literature ◽

Real Life ◽

Intelligent Agent ◽

Difficult Problem ◽

Black Box ◽

Machine Intelligence ◽

Agent Based ◽

Np Hard Problem ◽

Box Method

Current machine intelligence metrics rely on a different philosophy, hindering their effective comparison. There is no standardization of what is machine intelligence and what should be measured to quantify it. In this study, we investigate the measurement of intelligence from the viewpoint of real-life difficult-problem-solving abilities, and we highlight the importance of being able to make accurate and robust comparisons between multiple cooperative multiagent systems (CMASs) using a novel metric. A recent metric presented in the scientific literature, called MetrIntPair, is capable of comparing the intelligence of only two CMASs at an application. In this paper, we propose a generalization of that metric called MetrIntPairII. MetrIntPairII is based on pairwise problem-solving intelligence comparisons (for the same problem, the problem-solving intelligence of the studied CMASs is evaluated experimentally in pairs). The pairwise intelligence comparison is proposed to decrease the necessary number of experimental intelligence measurements. MetrIntPairII has the same properties as MetrIntPair, with the main advantage that it can be applied to any number of CMASs conserving the accuracy of the comparison, while it exhibits enhanced robustness. An important property of the proposed metric is the universality, as it can be applied as a black-box method to intelligent agent-based systems (IABSs) generally, not depending on the aspect of IABS architecture. To demonstrate the effectiveness of the MetrIntPairII metric, we provide a representative experimental study, comparing the intelligence of several CMASs composed of agents specialized in solving an NP-hard problem.

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