Ontology acquisition and exchange of evolutionary product-brokering agents

Agent-based electronic commerce (e-commerce) has been booming with the development of the Internet and agent technologies. However, little effort has been devoted to exploring the learning and evolving capabilities of software agents. This paper addresses issues of evolving software agents in e-commerce applications. An agent structure with evolution features is proposed with a focus on internal hierarchical knowledge. We argue that knowledge base of agents should be the cornerstone for their evolution capabilities, and agents can enhance their knowledge bases by exchanging knowledge with other agents. In this paper, product ontology is chosen as an instance of knowledge base. We propose a new approach to facilitate ontology exchange among e-commerce agents. The ontology exchange model and its formalities are elaborated. Product-brokering agents have been designed and implemented, which accomplish the ontology exchange process from request to integration

Class decomposition for GA-based classifier agents – A Pitt approach

Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multi-agent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an “integration” operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed

A multi-agent architecture for electronic payment

The Internet has brought about innumerable changes to the way enterprises do business. An essential problem to be solved before the widespread commercial use of the Internet is to provide a trustworthy solution for electronic payment. We propose a multi-agent mediated electronic payment architecture in this paper. It is aimed at providing an agent-based approach to accommodate multiple e-payment schemes. Through a layered design of the payment structure and a well-defined uniform payment interface, the architecture shows good scalability. When a new e-payment scheme or implementation is available, it can be plugged into the framework easily. In addition, we construct a framework allowing multiple agents to work cooperatively to realize automation of electronic payment. A prototype has been built to illustrate the functionality of this design. Finally we discuss the security issues

An incremental approach to genetic algorithms based classification

Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multi-agent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an “integration” operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed

Cooperative co-evolution of GA-based classifiers based on input increments

Genetic algorithms (GAs) have been widely used as soft computing techniques in various applications, while cooperative co-evolution algorithms were proposed in the literature to improve the performance of basic GAs. In this paper, a new cooperative co-evolution algorithm, namely ECCGA, is proposed in the application domain of pattern classification. Concurrent local and global evolution and conclusive global evolution are proposed to improve further the classification performance. Different approaches of ECCGA are evaluated on benchmark classification data sets, and the results show that ECCGA can achieve better performance than the cooperative co-evolution genetic algorithm and normal GA. Some analysis and discussions on ECCGA and possible improvement are also presented

A modularized electronic payment system for agent-based e-commerce

With the explosive growth of the Internet, electronic-commerce (e-commerce) is an increasingly important segment of commercial activities on the web. The Secure Agent Fabrication, Evolution & Roaming (SAFER) architecture was proposed to further facilitate e-commerce using agent technology. In this paper, the electronic payment aspect of SAFER will be explored. The Secure Electronic Transaction (SET) protocol and E-Cash were selected as the bases for the electronic payment system implementation. The various modules of the payment system and how they interface with each other are shown. An extensible implementation done using JavaTM will also be elaborated. This application incorporates agent roaming functionality and the ability to conduct e-commerce transactions and carry out intelligent e-payment procedures

Modular feature selection using relative importance factors

Feature selection plays an important role in finding relevant or irrelevant features in classification. Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems. In this paper, we explore the use of feature selection in modular GA-based classification. We propose a new feature selection technique, Relative Importance Factor (RIF), to find irrelevant features in the feature space of each module. By removing these features, we aim to improve classification accuracy and reduce the dimensionality of classification problems. Benchmark classification data sets are used to evaluate the proposed approaches. The experiment results show that RIF can be used to determine irrelevant features and help achieve higher classification accuracy with the feature space dimension reduced. The complexity of the resulting rule sets is also reduced which means the modular classifiers with irrelevant features removed will be able to classify data with a higher throughput

Finite element modelling and load share analysis for involute worm gears with localised tooth contact

A new approach has been developed by the authors to estimate the load share of worm gear drives, and to calculate the instantaneous tooth meshing stiffness and loaded transmission errors. In the approach, the finite element (FE) modelling is based on the modified tooth geometry, which ensures that the worm gear teeth are in localized contact. The geometric modelling method for involute worm gears allows the tooth elastic deformation and tooth root stresses of worm gear drives under different load conditions to be investigated. On the basis of finite element analysis, the instantaneous meshing stiffness and loaded transmission errors are obtained and the load share is predicted. In comparison with existing methods, this approach applies loaded tooth contact analysis and provides more accurate load capacity rating of worm gear drives

The LHC Discovery Potential of a Leptophilic Higgs

In this work, we examine a two-Higgs-doublet extension of the Standard Model in which one Higgs doublet is responsible for giving mass to both up- and down-type quarks, while a separate doublet is responsible for giving mass to leptons. We examine both the theoretical and experimental constraints on the model and show that large regions of parameter space are allowed by these constraints in which the effective couplings between the lightest neutral Higgs scalar and the Standard-Model leptons are substantially enhanced. We investigate the collider phenomenology of such a "leptophilic" two-Higgs-doublet model and show that in cases where the low-energy spectrum contains only one light, CP-even scalar, a variety of collider processes essentially irrelevant for the discovery of a Standard Model Higgs boson (specifically those in which the Higgs boson decays directly into a charged-lepton pair) can contribute significantly to the discovery potential of a light-to-intermediate-mass (m_h < 140 GeV) Higgs boson at the LHC.Comment: 25 pages, LaVTeX, 11 figures, 1 tabl

Cost-Effectiveness of Advanced Imaging Technologies in the Presurgical Workup of Epilepsy.

The cost-effectiveness and benefit of many diagnostic tests used in the presurgical evaluation for persons with epilepsy is for the most part uncertain as is their influence on decision-making. The options we have at our disposal are ever increasing. Advanced imaging modalities aim to improve surgical candidacy by helping us better define the epileptogenic zone and optimize surgical planning. However, judicious use is important. Randomized controlled trials delineating which mode of investigation is superior are lacking. Presurgical tests do have incremental value by increasing surgical candidacy and refining surgical planning. The yield of additional imaging will increase with complex localization. However, every case must be tailored by hypothesis, cost, and accessibility. Future studies using a quantitative cost-benefit framework are needed to determine the cost-effectiveness of advanced diagnostic tests (beyond magnetic resonance imaging) in the presurgical evaluation of those with epilepsy