Desde01 janeiro 2010
CentroLaboratório de Inteligência Artificial e Apoio à Decisão
Neto, J; Morais, AJ; Gonçalves, R; Coelho, AL;
Communications in Computer and Information Science
The herein proposed research project brings together the area of the multi-agent recommender systems and the IoT and aims to study the extent to which a context-based multi-agent recommender system can contribute to improving efficiency in the evacuation of buildings under a fire emergency, recommending the most adequate and efficient evacuation routes in real time. © Springer Nature Switzerland AG 2019.
Monteiro, A; Morais, AJ; Nunes, M; Dias, D;
Neto, J; Morais, AJ;
DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, 11TH INTERNATIONAL CONFERENCE
Due to the large amount of pages in Websites it is important to collect knowledge about users' previous visits in order to provide patterns that allow the customization of the Website. In previous work we proposed a multi-agent approach using agents with two different algorithms (associative rules and collaborative filtering) and showed the results of the offline tests. Both algorithms are incremental and work with binary data. In this paper we present the results of experiments held online. Results show that this multi-agent approach combining different algorithms is capable of improving user's satisfaction.
A. Jorge Morais; Eugénio Oliveira; Alípio Jorge
DCAI'12 - 9th International Symposium on Distributed Computing and Artificial Intelligence, vol.151, pp.281-288, Salamanca, Spain
The large amount of pages in Websites is a problem for users who waste time looking for the information they really want. Knowledge about users' previous visits may provide patterns that allow the customization of the Website. This concept is known as Adaptive Website: a Website that adapts itself for the purpose of improving the user's experience. Some Web Mining algorithms have been proposed for adapting a Website. In this paper, a recommender system using agents with two different algorithms (associative rules and collaborative filtering) is described. Both algorithms are incremental and work with binary data. Results show that this multi-agent approach combining different algorithms is capable of improving user's satisfaction.
A. Jorge Morais
PAAMS 2009 - 7th International Conference on Practical Applications of Agents and Multi-Agent Systems, vol.55, pp.349-355, Salamanca, Spain
Web growth has brought several problems to users. The large amount of information that exists nowadays in some particular Websites turns the task of finding useful information very difficult. Knowing users' visiting pattern is crucial to owners, so that they may transform or customize the Website. This problem originated the concept known as Adaptive Website: a Website that adapts itself for the purpose of improving the user's experience. This paper describes a proposal for a doctoral thesis. The main goal of this work is to follow a multi-agent approach for Web adaptation. The idea is that all knowledge administration about the Website and its users, and the use of that knowledge to adapt the site to fulfil user's needs, are made by an autonomous intelligent agent society in a negotiation environment. The complexity of the problem and the inherently distributed nature of the Web, which is an open, heterogeneous and decentralized network, are reasons that justify the multi-agent approa
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