Finding reliable partners to interact with in open environments is a challenging task for software agents, and trust and reputation mechanisms are used to handle this issue. From this viewpoint, we can observe the growing body of research on this subject, which indicates that these mechanisms can be considered key elements to design multiagent systems (MASs). Based on that, this article presents an extensive but not exhaustive review about the most significant trust and reputation models published over the past two decades, and hundreds of models were analyzed using two perspectives. The first one is a combination of trust dimensions and principles proposed by some relevant authors in the field, and the models are discussed using an MAS perspective. The second one is the discussion of these dimensions taking into account some types of interaction found in MASs, such as coalition, argumentation, negotiation, and recommendation. By these analyses, we aim to find significant relations between trust dimensions and types of interaction so it would be possible to construct MASs using the most relevant dimensions according to the types of interaction, which may help developers in the design of MASs.

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Trust allows the behavior evaluation of individuals by setting confidence values, which are used in decisions about whether or not to interact. They have been used in several fields, and many trust and reputation models were developed recently. We perceived that most of them were built upon the numeric and cognitive paradigms, which do not use affective aspects to build trust or help in decision making. Studies in psychology argued that personality, emotions, and mood are important in decision making and can change the behaviors of individuals. Based on that, we present links between trust and affective computing, showing relations of trust dimensions found in trust models with affective aspects, and presenting why affective computing approaches fit trust issues often addressed by research in this field. We also discuss trust relationships and decision making, emotions, and personality. Affective computing concepts have been used in a dispersed way and specifically in some models, so we aim to bring them together to encourage the growth of fuller trust models similar to those used by humans. We aim to find relations between both fields so it will be possible to employ such concepts to develop trust models using this new paradigm, defined as the affective paradigm.

Resumo. Este artigo descreve um comparativo entre dois algoritmos da área de mineração de textos, os quais são utilizados na tarefa de sumarização automática de documentos. Foram comparados nos experimentos o algoritmo clássico de Luhn e o algoritmo GistSumm, sendo realizadas dois tipos de avaliação, ambas utilizando o Português do Brasil como idioma alvo. A primeira consistiu em gerar um resumo de um texto fonte com cada algoritmo,e a avaliação foi conduzida utilizando avaliadores humanos que indicaram a coerência nos resumos de cada um. Por outro lado, a segunda foi conduzida por meio de uma avaliação baseada no resumo, no qual os avaliadores responderam perguntas sobre o texto original possuindo como fonte de consulta somente o resumo gerado pelos algoritmos. Após as análises, foi demonstrado que o algoritmo GistSumm possui maior capacidade para gerar resumos que mantenham a ideia principal do texto, sendo classificado com 81,6% de eficiência no primeiro experimento e 90% no segundo experimento.