93,377 research outputs found
Sistema de recomendação Web usando agentes
O crescimento da Web trouxe vários problemas aos utilizadores. A grande quantidade de informação existente hoje em dia em alguns sĂtios Web torna a procura de informação Ăştil muito difĂcil. Os objetivos dos proprietários dos sĂtios Web e dos utilizadores nem sempre coincidem. O conhecimento dos padrões de visitas dos utilizadores Ă© crucial para que os proprietários possam transformar e adaptar o sĂtio Web. Este Ă© o princĂpio do sĂtio Web adaptativo: o sĂtio Web adapta-se de forma a melhorar a experiĂŞncia do utilizador. Alguns algoritmos foram propostos para adaptar um sĂtio da Web. Neste artigo, descrevemos uma proposta de um sistema de recomendação Web baseado em agentes que combina dois algoritmos: regras de associação e filtragem colaborativa. Ambos os algoritmos sĂŁo incrementais e funcionam com dados binários. Os resultados mostram que, em algumas situações, a abordagem multiagente melhora a capacidade preditiva quando comparada com os agentes individuais.The growth of the Web has brought several problems for users. Today the vast amount of information on some web sites makes useful information finding very difficult. The objectives of the owners of the web sites and users do not always coincide. The knowledge of patterns of user visits is crucial to the owners to transform and adapt their web site. This is the adaptive website principle: the website adapts to improve the user experience. Some algorithms have been proposed to tailor a website. In this paper, we describe a proposal for a web recommendation system based on agents that combines two algorithms: association rules and collaborative filtering. Both algorithms are incremental and work with binary data. The results show that, in some situations, the multi-agent approach overcomes the predictive capacity of individual agents
An Expressive Language and Efficient Execution System for Software Agents
Software agents can be used to automate many of the tedious, time-consuming
information processing tasks that humans currently have to complete manually.
However, to do so, agent plans must be capable of representing the myriad of
actions and control flows required to perform those tasks. In addition, since
these tasks can require integrating multiple sources of remote information ?
typically, a slow, I/O-bound process ? it is desirable to make execution as
efficient as possible. To address both of these needs, we present a flexible
software agent plan language and a highly parallel execution system that enable
the efficient execution of expressive agent plans. The plan language allows
complex tasks to be more easily expressed by providing a variety of operators
for flexibly processing the data as well as supporting subplans (for
modularity) and recursion (for indeterminate looping). The executor is based on
a streaming dataflow model of execution to maximize the amount of operator and
data parallelism possible at runtime. We have implemented both the language and
executor in a system called THESEUS. Our results from testing THESEUS show that
streaming dataflow execution can yield significant speedups over both
traditional serial (von Neumann) as well as non-streaming dataflow-style
execution that existing software and robot agent execution systems currently
support. In addition, we show how plans written in the language we present can
represent certain types of subtasks that cannot be accomplished using the
languages supported by network query engines. Finally, we demonstrate that the
increased expressivity of our plan language does not hamper performance;
specifically, we show how data can be integrated from multiple remote sources
just as efficiently using our architecture as is possible with a
state-of-the-art streaming-dataflow network query engine
Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor
The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities
Maps, agents and dialogue for exploring a virtual world
In previous years we have been involved in several projects in which users (or visitors) had to find their way in information-rich virtual environments. 'Information-rich' means that the users do not know beforehand what is available in the environment, where to go in the environment to find the information and, moreover, users or visitors do not necessarily know exactly what they are looking for. Information-rich means also that the information may change during time. A second visit to the same environment will require different behavior of the visitor in order for him or her to obtain similar information than was available during a previous visit. In this paper we report about two projects and discuss our attempts to generalize from the different approaches and application domains to obtain a library of methods and tools to design and implement intelligent agents that inhabit virtual environments and where the agents support the navigation of the user/visitor
The Web as an Adaptive Network: Coevolution of Web Behavior and Web Structure
Much is known about the complex network structure of the Web, and about behavioral dynamics on the Web. A number of studies address how behaviors on the Web are affected by different network topologies, whilst others address how the behavior of users on the Web alters network topology. These represent complementary directions of influence, but they are generally not combined within any one study. In network science, the study of the coupled interaction between topology and behavior, or state-topology coevolution, is known as 'adaptive networks', and is a rapidly developing area of research. In this paper, we review the case for considering the Web as an adaptive network and several examples of state-topology coevolution on the Web. We also review some abstract results from recent literature in adaptive networks and discuss their implications for Web Science. We conclude that adaptive networks provide a formal framework for characterizing processes acting 'on' and 'of' the Web, and offers potential for identifying general organizing principles that seem otherwise illusive in Web Scienc
Design Principals of Social Navigation
8th Delos Workshop on "User Interfaces for Digital Libraries" (on 21 October it will be held in conjuction with the 4th ERCIM Workshop on "User Interfaces for All"), SICS, Kista, Sweden, 21-23 October 1998PERSON
PACMAS: A Personalized, Adaptive, and Cooperative MultiAgent System Architecture
In this paper, a generic architecture, designed to
support the implementation of applications aimed at managing
information among different and heterogeneous sources,
is presented. Information is filtered and organized according
to personal interests explicitly stated by the user. User pro-
files are improved and refined throughout time by suitable
adaptation techniques. The overall architecture has been called
PACMAS, being a support for implementing Personalized, Adaptive,
and Cooperative MultiAgent Systems. PACMAS agents are
autonomous and flexible, and can be made personal, adaptive and
cooperative, depending on the given application. The peculiarities
of the architecture are highlighted by illustrating three relevant
case studies focused on giving a support to undergraduate and
graduate students, on predicting protein secondary structure, and
on classifying newspaper articles, respectively
Trail records and navigational learning
An emerging wave of 'ambient' technologies has the potential to support learning in new and particular ways. In this paper we propose a 'trail model' of 'navigational learning' which links some particular learning needs to the potentialities of these technologies. In this context, we outline the design and use of an 'experience recorder', a technology to support learning in museums. In terms of policy for the e-society, these proposals are relevant to the need for personalised and individualised learning support
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