Increasing the Intelligence of Virtual Sales Assistants through Knowledge Modeling Techniques

Shopping agents are web-based applications that help consumers to find appropriate products in the context of e-commerce. In this paper we argue about the utility of advanced model-based techniques that recently have been proposed in the fields of Artificial Intelligence and Knowledge Engineering, in order to increase the level of support provided by this type of applications. We illustrate this approach with a virtual sales assistant that dynamically configures a product according to the needs and preferences of customers.

COMUNICAÇÃO EM LINHA E RUÍDOS SEMÂNTICOS NA RECUPERAÇÃO DE INFORMAÇÕES EM PESQUISAS CIENTÍFICAS

O pesquisador científico necessita de informações precisas, em tempo hábil para conclusão de seus trabalhos. Com o advento da INTERNET, o processo de comunicação em linha, homem x máquina, mediado pelos mecanismos de busca, tornou-se, simultaneamente, um auxílio e uma dificuldade no processo de recuperação de informações. O pesquisador teve que adaptar-se ao modo de operar da INTERNET e incluiu conhecimentos de diferenças idiomáticas, de terminologia, além de utilizar instrumentos que lhe forneçam parâmetros para obter maior pertinência e relevância nos dados. O uso de agentes inteligentes para melhoria de resultados e a diminuição de ruídos semânticos têm sido apontados como soluções para aumento da precisão no resultado das buscas. O estudo de casos exploratório realizado analisa a pesquisa em linha a partir da teoria da informação e propõe duas formas de otimizar o processo comunicacional com vistas à pertinência e relevância dos dados obtidos: a primeira sugere a aplicação de algoritmos que utilizem o vocabulário controlado como mediador do processo de comunicação utilizando-se dos descritores para recuperação em linha. , e a segunda ressalta a importância dos agentes inteligentes no processo de comunicação homem-máquina.(AU)

COMUNICAÇÃO EM LINHA E RUÍDOS SEMÂNTICOS NA RECUPERAÇÃO DE INFORMAÇÕES EM PESQUISAS CIENTÍFICAS

O pesquisador científico necessita de informações precisas, em tempo hábil para conclusão de seus trabalhos. Com o advento da INTERNET, o processo de comunicação em linha, homem x máquina, mediado pelos mecanismos de busca, tornou-se, simultaneamente, um auxílio e uma dificuldade no processo de recuperação de informações. O pesquisador teve que adaptar-se ao modo de operar da INTERNET e incluiu conhecimentos de diferenças idiomáticas, de terminologia, além de utilizar instrumentos que lhe forneçam parâmetros para obter maior pertinência e relevância nos dados. O uso de agentes inteligentes para melhoria de resultados e a diminuição de ruídos semânticos têm sido apontados como soluções para aumento da precisão no resultado das buscas. O estudo de casos exploratório realizado analisa a pesquisa em linha a partir da teoria da informação e propõe duas formas de otimizar o processo comunicacional com vistas à pertinência e relevância dos dados obtidos: a primeira sugere a aplicação de algoritmos que utilizem o vocabulário controlado como mediador do processo de comunicação utilizando-se dos descritores para recuperação em linha. , e a segunda ressalta a importância dos agentes inteligentes no processo de comunicação homem-máquina.(AU)

COMUNICAÇÃO EM LINHA E RUÍDOS SEMÂNTICOS NA RECUPERAÇÃO DE INFORMAÇÕES EM PESQUISAS CIENTÍFICAS

O pesquisador científico necessita de informações precisas, em tempo hábil para conclusão de seus trabalhos. Com o advento da INTERNET, o processo de comunicação em linha, homem x máquina, mediado pelos mecanismos de busca, tornou-se, simultaneamente, um auxílio e uma dificuldade no processo de recuperação de informações. O pesquisador teve que adaptar-se ao modo de operar da INTERNET e incluiu conhecimentos de diferenças idiomáticas, de terminologia, além de utilizar instrumentos que lhe forneçam parâmetros para obter maior pertinência e relevância nos dados. O uso de agentes inteligentes para melhoria de resultados e a diminuição de ruídos semânticos têm sido apontados como soluções para aumento da precisão no resultado das buscas. O estudo de casos exploratório realizado analisa a pesquisa em linha a partir da teoria da informação e propõe duas formas de otimizar o processo comunicacional com vistas à pertinência e relevância dos dados obtidos: a primeira sugere a aplicação de algoritmos que utilizem o vocabulário controlado como mediador do processo de comunicação utilizando-se dos descritores para recuperação em linha. , e a segunda ressalta a importância dos agentes inteligentes no processo de comunicação homem-máquina.(AU)

Convergence and knowledge processing in multi-agent systems

Multi-agent systems are complex systems comprised of multiple intelligent agents that act either independently or in cooperation with one another. Agent-based modelling is a method for studying complex systems like economies, societies, ecologies etc. Due to their complexity, very often mathematical analysis is limited in its ability to analyse such systems. In this case, agent-based modelling offers a practical, constructive method of analysis. The objective of this book is to shed light on some emergent properties of multi-agent systems. The authors focus their investigation on the effect of knowledge exchange on the convergence of complex, multi-agent systems.

InfoStation-based Adaptable Provision of m Learning Services: Main Scenarios

This paper presents an adaptable InfoStation-based multi-agent system facilitating the mobile eLearning (mLearning) service provision within a University Campus. A horizontal view of the network architecture is presented. Main communications scenarios are considered by describing the detailed interaction of the system entities involved in the mLearning service provision. The mTest service is explored as a practical example. System implementation approaches are also considered.

Multi-Agent Security System based on Neural Network Model of User's Behavior

It is proposed an agent approach for creation of intelligent intrusion detection system. The system allows detecting known type of attacks and anomalies in user activity and computer system behavior. The system includes different types of intelligent agents. The most important one is user agent based on neural network model of user behavior. Proposed approach is verified by experiments in real Intranet of Institute of Physics and Technologies of National Technical University of Ukraine "Kiev Polytechnic Institute”.

Study of the Application of Neural Networks in Internet Traffic Engineering

In this study, we showed various approachs implemented in Artificial Neural Networks for network resources management and Internet congestion control. Through a training process, Neural Networks can determine nonlinear relationships in a data set by associating the corresponding outputs to input patterns. Therefore, the application of these networks to Traffic Engineering can help achieve its general objective: “intelligent” agents or systems capable of adapting dataflow according to available resources. In this article, we analyze the opportunity and feasibility to apply Artificial Neural Networks to a number of tasks related to Traffic Engineering. In previous sections, we present the basics of each one of these disciplines, which are associated to Artificial Intelligence and Computer Networks respectively.

e-Learning, e-Practising and e-Tutoring: an Integrated Approach

In this paper is described a didactic methodology combining current e-learning methods and the support of Intelligent Agents technologies. The aim is to favor the synthesis among theoretical approach and based practical approach using the so-called Intelligent Agent, software that exploits the Artificial Intelligence and that operates as tutor, facilitating the consumers in the training operations. The paper illustrates how such new Intelligent Agent algorithm (IA) is used in the training of employees working in the transportation sector, thanks to the experience gained with the PARMENIDE project - Promoting Advanced Resources and Methodologies for New Teaching and Learning Solutions in Digital Education.

Development of a Refactoring Learning Environment

This paper describes a Refactoring Learning Environment, which is intended to analyze and assess programming code, based on refactoring rules. The Refactoring Learning Environment architecture includes an intelligent assistant – Refactoring Agent, which is responsible for analysis and assessment of the code, written by students in real time by using a set of refactoring methods. According to the situation and based on the refactoring method, which should be applied, the agent could react in different ways. Its goal is to show the student, as much as possible, the weak places of his programming code and the possible ways to makes it better.

Maximising the efficiency of logistics operations with radio frequency identification technology

Radio frequency identification (RFID) technology has gained increasing popularity in businesses to improve operational efficiency and maximise costs saving. However, there is a gap in the literature exploring the enhanced use of RFID to substantially add values to the supply chain operations, especially beyond what the RFID vendors could offer. This paper presents a multi-agent system, incorporating RFID technology, aimed at fulfilling the gap. The system is developed to model supply chain activities (in particular, logistics operations) and is comprised of autonomous and intelligent agents representing the key entities in the supply chain. With the advanced characteristics of RFID incorporated, the agent system examines ways logistics operations (i.e. distribution network) particular) can be efficiently reconfigured and optimised in response to dynamic changes in the market, production and at any stage in the supply chain. © 2012 IEEE.

Agent-K: An Integration of AOP & KQML

Postprint

Sensor Intelligence for Tackling Energy-Drain Attacks on Wireless Sensor Networks

In this paper we propose a model for intelligent agents (sensors) on a Wireless Sensor Network to guard against energy-drain attacks in an energy-efficient and autonomous manner. This is intended to be achieved via an energy-harvested Wireless Sensor Network using a novel architecture to propagate knowledge to other sensors based on automated reasoning from an attacked sensor.

An Agent-based Classification Model

The major function of this model is to access the UCI Wisconsin Breast Cancer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classification can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artificial Immune Systems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to problem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifically for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based modelling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment.

Understanding Retail Productivity by Simulating Management Practices

Intelligent agents offer a new and exciting way of understanding the world of work. In this paper we apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents could offer potential for fostering sustainable organizational capabilities in the future. Our research so far has led us to conduct case study work with a top ten UK retailer, collecting data in four departments in two stores. Based on our case study data we have built and tested a first version of a department store simulator. In this paper we will report on the current development of our simulator which includes new features concerning more realistic data on the pattern of footfall during the day and the week, a more differentiated view of customers, and the evolution of customers over time. This allows us to investigate more complex scenarios and to analyze the impact of various management practices.