A study on like-attracts-like versus elitist selection criterion for human-like social behavior of memetic mulitagent systems

Memetic multi agent system emerges as an enhanced version of multiagent systems with the implementation of meme-inspired computational agents. It aims to evolve human-like behavior of multiple agents by exploiting the Dawkins' notion of a meme and Universal Darwinism. Previous research has developed a computational framework in which a series of memetic operations have been designed for implementing humanlike agents. This paper will focus on improving the human-like behavior of multiple agents when they are engaged in social interactions. The improvement is mainly on how an agent shall learn from others and adapt its behavior in a complex dynamic environment. In particular, we design a new mechanism that supervises how the agent shall select one of the other agents for the learning purpose. The selection is a trade-off between the elitist and like-attracts-like principles. We demonstrate the desirable interactions of multiple agents in two problem domains

Evolutionary Multiagent Transfer Learning With Model-Based Opponent Behavior Prediction

This article embarks a study on multiagent transfer learning (TL) for addressing the specific challenges that arise in complex multiagent systems where agents have different or even competing objectives. Specifically, beyond the essential backbone of a state-of-the-art evolutionary TL framework (eTL), this article presents the novel TL framework with prediction (eTL-P) as an upgrade over existing eTL to endow agents with abilities to interact with their opponents effectively by building candidate models and accordingly predicting their behavioral strategies. To reduce the complexity of candidate models, eTL-P constructs a monotone submodular function, which facilitates to select Top-K models from all available candidate models based on their representativeness in terms of behavioral coverage as well as reward diversity. eTL-P also integrates social selection mechanisms for agents to identify their better-performing partners, thus improving their learning performance and reducing the complexity of behavior prediction by reusing useful knowledge with respect to their partners' mind universes. Experiments based on a partner-opponent minefield navigation task (PO-MNT) have shown that eTL-P exhibits the superiority in achieving higher learning capability and efficiency of multiple agents when compared to the state-of-the-art multiagent TL approaches

Sistema de recomendação de objetos de aprendizagem digitais para e-learning: um estudo de caso em curso superior à distância da UFSC

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro de Ciências da Educação, Programa Pós-Graduação em Ciência da Informação, Florianópolis, 2017.A adoção de plataformas de e-learning (aprendizagem eletrônica) para a educação a distância (EaD) de universidades ao redor do mundo, tem sido uma das opções para estudantes que buscam flexibilidade e formação por meio da autoaprendizagem. Nesse aspecto, abre-se espaço para estudos de meios de trazer benefícios aos estudantes, dentre eles, promover acesso às informações que possam propiciar melhorias no fluxo contínuo no processo de aprendizagem. Um Sistema de Recomendação de Objetos de Aprendizagem Digitais possibilita contribuir com esse processo, além de atenuar as dificuldades face às complexidades nos processos de recuperação de informação relevante, devido à sobrecarga informacional nos repositórios dos cursos assim como na web. Os estudos acerca desses sistemas tem como premissa a sugestão de objetos, relacionados ao nível em que o estudante se encontra, como forma de apoiá-lo e contribuir para sua evolução. Nesta perspectiva, a presente pesquisa tem como objetivo geral ?Analisar como um Sistema de Recomendação de Objetos de Aprendizagem Digitais pode contribuir com estudantes de curso superior em modalidade de e-learning.?. Como estudo de caso, adota-se o curso de Licenciatura em Letras ? Espanhol (EaD/UFSC). No referencial teórico aborda-se a Recuperação de Informação, os Sistemas de Recomendação no contexto do e-learning, os Objetos de Aprendizagem Digitais e as tecnologias de Agentes Inteligentes e ainda, se estabelece articulação com a Ciência da Informação. A pesquisa caracteriza-se, quanto aos objetivos, como pesquisa bibliográfica de caráter exploratório que emprega a técnica de análise de conteúdo; quanto à forma de abordagem, como pesquisa qualitativa e quanto à revisão teórica, uma revisão sistemática. Como resultados foram elaborados o Modelo de Banco de Dados para armazenamento dos dados, bem como o Fluxo de Informação do Sistema de Recomendação para e-learning, para representar os processos de acesso às informações organizadas e armazenadas no banco. Um dos pontos relevantes, caracterizado neste trabalho, é o fato de que um Sistema de Recomendação permite ampliar o poder de recomendação de Objetos de Aprendizagem Digitais do professor. Sob a perspectiva de planejamento e organização da informação para uso, conclui-se ser viável a continuidade nas pesquisas para que os Sistemas de Recomendação possam ser adotados não somente no ambiente de estudo de caso desta pesquisa, assim como em quaisquer outros ambientes de aprendizagem com adoção de novas formas (modelos, técnicas, alvos, entre outros) de implementação.Abstract : The adoption of e-learning platforms for distance education by universities around the world has been one of the options for students seeking flexibility and training through self-learning. In this context, research is required on ways to benefit the students, promoting access to information that can improve the continuous flow of the learning process. A Recommender System of Digital Learning Objects can contribute to this process and, additionally, alleviate the difficulties faced in the complex processes of retrieving relevant information, due to the informational overload in the course?s repositories as well as on the web. Studies about these systems are based on the suggestion of objects, related to the level at which the student is at, as a way to support and contribute to their evolution. In this perspective, the present research has as general objective: "To analyze how a Recommender System of Digital Learning Objects can support the students of e-learning graduation course". As a case study, it has been chosen the e-learning degree in Letters - Spanish (EaD / UFSC). In the theoretical reference it is addressed the Information Retrieval, the Recommender Systems in the context of e-learning, the Digital Learning Objects and the Intelligent Agents technologies, and it is also established the relation with Information Science. The research is characterized as regards to the objectives as: an exploratory bibliographic research that employs the technique of content analysis; qualitative research; and a systematic review. As result, the Database Model was developed, as well as the Information Flow of the Recommender System for e-learning, to represent the processes for accessing the information organized and stored in the database. One of the key points characterized in this work is the fact that a Recommender System improves the quality of the teacher?s recommendations of Digital Learning Objects. From the perspective of planning and organizing information, it was concluded that follow up research is viable so that Recommender Systems can be adopted not only in the case study of this research, but in any other learning environment adopting new methods (models, techniques, targets, among others) of implementation

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

A multi-agent memetic system for human-based knowledge selection

In these last decades, both industrial and academic organizations have used extensively different learning methods to improve humans' capabilities and, as consequence, their overall performance and competitiveness in the new economy context. However, the rapid change in modern knowledge due to exponential growth of information sources is complicating learners' activity. At the same time, new technologies offer, if used in a right way, a range of possibilities for the efficient design of learning scenarios. For that reason, novel approaches are necessary to obtain suitable learning solutions which are able to generate efficient, personalized, and flexible learning experiences. From this point of view, computational intelligence methodologies can be exploited to provide efficient and intelligent tools to be able to analyze learner's needs and preferences and, consequently, personalize its knowledge acquirement. This paper reports an attempt to achieve these results by exploiting an ontological representation of learning environment and an adaptive memetic approach, integrated into a cooperative multi-agent framework. In particular, a collection of agents analyzes learner preferences and generate high-quality learning presentations by executing, in a parallel way, different cooperating optimization strategies. This cooperation is performed by jointly exploiting data mining via fuzzy decision trees, together with a decision-making framework exploiting fuzzy methodologies

Novel Memetic Computing Structures for Continuous Optimisation

This thesis studies a class of optimisation algorithms, namely Memetic Computing Structures, and proposes a novel set of promising algorithms that move the first step towards an implementation for the automatic generation of optimisation algorithms for continuous domains. This thesis after a thorough review of local search algorithms and popular meta-heuristics, focuses on Memetic Computing in terms of algorithm structures and design philosophy. In particular, most of the design carried out during my doctoral studies is inspired by the lex parsimoniae, aka Ockham’s Razor. It has been shown how simple algorithms, when well implemented can outperform complex implementations. In order to achieve this aim, the design is always carried out by attempting to identify the role of each algorithmic component/operator. In this thesis, on the basis of this logic, a set of variants of a recently proposed algorithms are presented. Subsequently a novel memetic structure, namely Parallel Memetic Structure is proposed and tested against modern algorithms representing the state of the art in optimisation. Furthermore, an initial prototype of an automatic design platform is also included. This prototype performs an analysis on separability of the optimisation problem and, on the basis of the analysis results, designs some parts of the parallel structure. Promising results are included. Finally, an investigation of the correlation among the variables and problem dimensionality has been performed. An extremely interesting finding of this thesis work is that the degree of correlation among the variables decreases when the dimensionality increases. As a direct consequence of this fact, large scale problems are to some extent easier to handle than problems in low dimensionality since, due to the lack of correlation among the variables, they can effectively be tackled by an algorithm that performs moves along the axes