Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Marcio K. Crocomo"'
Autor:
Ricardo C. Joaquim, Kleber de Oliveira Andrade, Marcio K. Crocomo, Glauco Augusto de Paula Caurin
Publikováno v:
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Universidade de São Paulo (USP)
instacron:USP
This article proposes the use of two evolutionary algorithms (EAs) to the dynamic difficulty adjustment (DDA) of a serious game in the rehabilitation robotics application. DDA occurs in runtime for a better user experience with a game. This approach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5ee660759ec489d4ddfe342960f8dcb
https://doi.org/10.1145/3180657
https://doi.org/10.1145/3180657
Publikováno v:
SBGames
Based on the Evolutionary Algorithm (EA) proposed by [1], this paper presents a new EA version with dynamic difficulty adjustment for a Whac-a-Mole like game used in motor rehabilitation of hand. This new version considers user performance as input a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a682a4893ccba7340c3e69ce84e3ac13
https://doi.org/10.1109/sbgames.2018.00020
https://doi.org/10.1109/sbgames.2018.00020
Autor:
Kleber de Oliveira Andrade, Glauco Augusto de Paula Caurin, Thales B. Pasqual, Marcio K. Crocomo
Publikováno v:
SeGAH
This article explores game difficulty adjustment for serious game applications in rehabilitation robotics. In this context, a difficulty adjustment system is proposed that takes user performance as input and generates two different responses: a) a ch
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::077312b694ad0705614045d606741353
https://doi.org/10.1109/segah.2016.7586277
https://doi.org/10.1109/segah.2016.7586277
Publikováno v:
International Journal of Natural Computing Research. 3:1-19
Estimation of Distribution Algorithms (EDAs) have proved themselves as an efficient alternative to Genetic Algorithms when solving nearly decomposable optimization problems. In general, EDAs substitute genetic operators by probabilistic sampling, ena
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e70f8066d5f1cb83fa78291662909d4c
https://doi.org/10.4018/jncr.2012100101
https://doi.org/10.4018/jncr.2012100101
This paper presents a new technique for optimizing binary problems with building blocks. The authors have developed a different approach to existing Estimation of Distribution Algorithms (EDAs). Our technique, called Phylogenetic Differential Evoluti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24bdadb5cb41f9b96fd663ae729b21d4
https://doi.org/10.4018/978-1-4666-4253-9.ch002
https://doi.org/10.4018/978-1-4666-4253-9.ch002
Autor:
Constancio Bringel Neto, Karla Vittori, Jean Paulo Martins, Alexandre C. B. Delbem, Marcio K. Crocomo
Publikováno v:
IEEE Congress on Evolutionary Computation
Linkage Learning (LL) was proposed as a methodology to enable Genetic Algorithms (GAs) to solve complex optimization problems more effectively. Its main idea relies on a reductionist assumption, considering optimization problems as being composed of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f38f30f0126226bc606e55a6e2b84661
https://doi.org/10.1109/cec.2013.6557610
https://doi.org/10.1109/cec.2013.6557610