Challenges for adaptation in agent societies

The final publication is available at Springer via http://dx.doi.org/[insert DOIAdaptation in multiagent systems societies provides a paradigm for allowing these societies to change dynamically in order to satisfy the current requirements of the system. This support is especially required for the next generation of systems that focus on open, dynamic, and adaptive applications. In this paper, we analyze the current state of the art regarding approaches that tackle the adaptation issue in these agent societies. We survey the most relevant works up to now in order to highlight the most remarkable features according to what they support and how this support is provided. In order to compare these approaches, we also identify different characteristics of the adaptation process that are grouped in different phases. Finally, we discuss some of the most important considerations about the analyzed approaches, and we provide some interesting guidelines as open issues that should be required in future developments.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, the European Cooperation in the field of Scientific and Technical Research IC0801 AT, and projects TIN2009-13839-C03-01 and TIN2011-27652-C03-01.Alberola Oltra, JM.; Julian Inglada, VJ.; García-Fornes, A. (2014). Challenges for adaptation in agent societies. Knowledge and Information Systems. 38(1):1-34. https://doi.org/10.1007/s10115-012-0565-yS134381Aamodt A, Plaza E (1994) Case-based reasoning; foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59Abdallah S, Lesser V (2007) Multiagent reinforcement learning and self-organization in a network of agents. 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Abordagem multiagente em sistemas de recomendação Web

Dissertação de Mestrado em Tecnologias e Sistemas Informáticos Web apresentada à Universidade AbertaO crescimento exponencial da informação disponível na Web torna difícil para os utilizadores a tarefa de obter a informação que pretendem e quando dela necessitam. Para ultrapassar o problema, os sítios Web têm vindo a incorporar sistemas de recomendação que, baseados no histórico de acessos, têm como objetivo maximizar a satisfação dos utilizadores, disponibilizando-lhes recomendações de alta qualidade. A complexidade do problema e a natureza distribuída da Web justificam abordagens baseadas na tecnologia dos agentes inteligentes autónomos e sistemas multiagente, permitindo combinar múltiplos algoritmos de recomendação, aumentando assim as hipóteses das recomendações sugeridas serem efetivamente do interesse do utilizador. É este o tipo de abordagem explorada pelo sistema de recomendação multiagente AMAAFWA (A Multi-Agent Approach for Web Adaptation) (Morais, 2013). Os testes realizados em modo offline mostraram que essa abordagem multiagente, baseada em agentes implementando diferentes algoritmos, apresenta um desempenho superior ao dos algoritmos considerados individualmente. O objetivo desta dissertação é adaptar e testar o sistema AMAAFWA em tempo real, com o objetivo de validar os resultados obtidos em modo offline, pelo que se procedeu à sua adaptação para funcionamento online, integrando-o num sítio Web. O sistema AMAAFWA baseia-se numa classificação implícita dos itens e os algoritmos de recomendação são baseados em memória e incrementais. Foi também criada e testada uma versão do sistema que considera uma classificação explícita dos itens por parte dos utilizadores, com o propósito de comparar o desempenho de ambos os tipos de classificação. Demonstra-se na presente dissertação que o sistema de recomendação multiagente AMAAFWA, em funcionamento online, apresenta um desempenho superior ao dos algoritmos considerados individualmente, sendo ainda capaz de melhorar a satisfação do utilizador e contribuir para o aumento do sucesso do sítio Web em que se insere. Relativamente à comparação dos tipos de classificação implícita e explícita dos itens, os resultados mostram desempenhos similares.The exponential growth of information available on the Web makes it difficult for users to get the information they want and when they need it. To overcome the problem, the Web sites are using recommender systems in order to provide high-quality recommendations to the users and, in that way, improve user satisfaction. The complexity of the problem and the distributed nature of Web justify the use of the autonomous intelligent agents and multi-agent systems technology approaches, which allows the combination of multiple recommendation algorithms in order to increase the chances of the suggested recommendations to be actually of interest to the users. The multi-agent recommender system AMAAFWA (A Multi-Agent Approach for Web Adaptation) (Morais, 2013) explores this approach. The results of the tests performed offline showed that this multi-agent approach, based on agents implementing different algorithms, has a higher performance when compared to individual algorithms. The goal of this dissertation is to adapt and test the AMAAFWA system in real-time operation, in order to validate the results obtained in offline mode. So, we adapted the system for online operation and integrate it on a website. The AMAAFWA system is based on implicit classification of items and the recommendation algorithms are memory and item-based and incremental. It was also built and tested a version of the system that considers explicit classification of items by users, with the aim of comparing the performance of both types of classification. It is shown in this dissertation that the multi-agent recommender system AMAAFWA, in online and real-time operation, has a higher performance when compared to individual algorithms, being able to improve user satisfaction and contribute to the increasing success of the website. Concerning the comparison between implicit and explicit classification, the results show a similar performance for both

Biology of Applied Digital Ecosystems

A primary motivation for our research in Digital Ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. However, the biological processes that contribute to these properties have not been made explicit in Digital Ecosystems research. Here, we discuss how biological properties contribute to the self-organising features of biological ecosystems, including population dynamics, evolution, a complex dynamic environment, and spatial distributions for generating local interactions. The potential for exploiting these properties in artificial systems is then considered. We suggest that several key features of biological ecosystems have not been fully explored in existing digital ecosystems, and discuss how mimicking these features may assist in developing robust, scalable self-organising architectures. An example architecture, the Digital Ecosystem, is considered in detail. The Digital Ecosystem is then measured experimentally through simulations, with measures originating from theoretical ecology, to confirm its likeness to a biological ecosystem. Including the responsiveness to requests for applications from the user base, as a measure of the 'ecological succession' (development).Comment: 9 pages, 4 figure, conferenc

Distributed sensor-driven web applications through multi-device usage patterns

C

A short curriculum of the robotics and technology of computer lab

Our research Lab is directed by Prof. Anton Civit. It is an interdisciplinary group of 23 researchers that carry out their teaching and researching labor at the Escuela Politécnica Superior (Higher Polytechnic School) and the Escuela de Ingeniería Informática (Computer Engineering School). The main research fields are: a) Industrial and mobile Robotics, b) Neuro-inspired processing using electronic spikes, c) Embedded and real-time systems, d) Parallel and massive processing computer architecture, d) Information Technologies for rehabilitation, handicapped and elder people, e) Web accessibility and usability In this paper, the Lab history is presented and its main publications and research projects over the last few years are summarized.Nuestro grupo de investigación está liderado por el profesor Civit. Somos un grupo multidisciplinar de 23 investigadores que realizan su labor docente e investigadora en la Escuela Politécnica Superior y en Escuela de Ingeniería Informática. Las principales líneas de investigaciones son: a) Robótica industrial y móvil. b) Procesamiento neuro-inspirado basado en pulsos electrónicos. c) Sistemas empotrados y de tiempo real. d) Arquitecturas paralelas y de procesamiento masivo. e) Tecnología de la información aplicada a la discapacidad, rehabilitación y a las personas mayores. f) Usabilidad y accesibilidad Web. En este artículo se reseña la historia del grupo y se resumen las principales publicaciones y proyectos que ha conseguido en los últimos años

A Self-adaptive Agent-based System for Cloud Platforms

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