DSpace logo

Use este identificador para citar ou linkar para este item: http://repositorioinstitucional.uea.edu.br//handle/riuea/4015
Registro completo de metadados
Campo DCValorIdioma
dc.contributor.authorChen, Daniel Akio-
dc.date.available2022-06-27-
dc.date.available2022-06-30T20:20:21Z-
dc.date.issued2022-05-27-
dc.identifier.urihttp://repositorioinstitucional.uea.edu.br//handle/riuea/4015-
dc.description.abstractDue to the growing number of people consuming electronic games it is inevitable the gaming industry will grow as well. With the increased access to video games, many healthcare professionals have taken advantage of them as tools to support patient care. However, many games are still not used in these procedures because health professionals do not have assessments that provide parameters for deciding whether the level of difficulty of a particular game is appropriate to the profile of the patient being treated. This work presents a game neuro-evolution method, a way to build neural networks for solving and evaluating games, and the steps used to build such a tool.pt_BR
dc.languageporpt_BR
dc.publisherUniversidade do Estado do Amazonaspt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectInteligência Artificialpt_BR
dc.subjectNeuro-evoluçãopt_BR
dc.subjectJogospt_BR
dc.subjectArtificial Intelligencept_BR
dc.subjectNeuro-evolutionpt_BR
dc.subjectGamespt_BR
dc.titleNeuro-evolução aplicada na conclusão de jogos de plataformapt_BR
dc.title.alternativeNeuro-evolution applied to platform game completionpt_BR
dc.typeTrabalho de Conclusão de Cursopt_BR
dc.date.accessioned2022-06-30T20:20:21Z-
dc.creator.ID4530225226763667pt_BR
dc.contributor.advisor1Carmo, Ricardo Rios Monteiro do-
dc.contributor.advisor1ID2619630457069143pt_BR
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/2619630457069143pt_BR
dc.contributor.referee1Carmo, Ricardo Rios Monteiro do-
dc.contributor.referee1ID2619630457069143pt_BR
dc.contributor.referee1Latteshttp://lattes.cnpq.br/2619630457069143pt_BR
dc.contributor.referee2Costa, Elloá Barreto Guedes-
dc.contributor.referee2ID6466781778573760pt_BR
dc.contributor.referee2Latteshttp://lattes.cnpq.br/6466781778573760pt_BR
dc.contributor.referee3Silva, Fábio Santos da-
dc.contributor.referee3ID5711873110376600pt_BR
dc.contributor.referee3Latteshttp://lattes.cnpq.br/5711873110376600pt_BR
dc.creator.Latteshttp://lattes.cnpq.br/4530225226763667pt_BR
dc.description.resumoCom o crescente número de pessoas que consomem jogos eletrônicos é inevitável que a indústria de jogos eletrônicos também cresça. Com o aumento de acesso aos jogos eletrônicos, diversos profissionais de saúde têm se favorecido deles como ferramentas de apoio ao tratamento de pacientes. Contudo, muitos jogos ainda não são utilizados nesses procedimentos, porque os profissionais de saúde não contam com avaliações que ofereçam parâmetros para decidir se o nível de dificuldade de um determinado jogo está adequado ao perfil do paciente em tratamento. Esse trabalho apresenta um método de neuro-evolução de jogos, uma maneira de construir redes neurais para resolução e avaliação de jogos, além dos passos utilizados para construir tal ferramenta.pt_BR
dc.publisher.countryBrasilpt_BR
dc.relation.referencesABANDONWARE, M. CHESSMASTER 8000. 2000. Dispon´ıvel em: <https://www. myabandonware.com/game/chessmaster-8000-doa>. Acesso em: 3 de Mar¸co de 2022. ABSTRACTS. Biometrics, [Wiley, International Biometric Society], v. 21, n. 3, p. 761–777, 1965. ISSN 0006341X, 15410420. Dispon´ıvel em: <http://www.jstor.org/stable/2528559>. ANGELINE, P. J. et al. An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks, 1994. APONTE, M.-V.; LEVIEUX, G.; NATKIN, S. Measuring the level of difficulty in single player video games. Entertainment Computing, v. 2, 01 2009. AUTHORS, S. Super Mario Bros.:RAM map. 2005. Dispon´ıvel em: <https://datacrystal. romhacking.net/wiki/Super\ Mario\ Bros.:RAM\ map>. Acesso em: 20 de dezembro de 2021. BALLARD CLIVE G.; CORBETT, A. C. H. O. A. Can brain training games improve cognition in peope over 60? Alzheimer’s & Dementia, Elsevier Science, v. 6, p. e55–e56, 2010. ISSN 1552-5260. Dispon´ıvel em: <http://doi.org/10.1016/j.jalz.2010.08.171>. BARROSO, S. M. et al. Treinamento cognitivo de aten¸c˜ao e mem´oria de universit´arios com jogos eletrˆonicos. Psico, v. 50, n. 4, p. e29466–e29466, 2019. BERNER, C. et al. Dota 2 with large scale deep reinforcement learning. arXiv preprint arXiv:1912.06680, 2019. BORG, P. J. F. G. I. Modern Multidimensional Scaling: Theory and Applications. 2nd. ed. [S.l.]: Springer, 2005. (Springer Series in Statistics). ISBN 9780387251509,0387251502. BRAUN, H.; WEISBROD, J. Evolving neural feedforward networks. 1993. BRINK JOSEPH RICHARDS, M. F. H. Real-World Machine Learning. 1. ed. Manning Publications, 2016. ISBN 1617291927,9781617291920. Dispon´ıvel em: <http://gen.lib.rus.ec/ book/index.php?md5=f22a7835b960bceb3b248a89c864da48>. BRO, R.; SMILDE, A. K. Principal component analysis. Analytical methods, Royal society of chemistry, v. 6, n. 9, p. 2812–2831, 2014. CARDOSO, N. d. O.; LANDENBERGER, T.; ARGIMON, I. I. de L. Jogos eletrˆonicos como instrumentos de interven¸c˜ao no decl´ınio cognitivo–uma revis˜ao sistem´atica. Revista de Psicologia da IMED, Faculdade Meridional-IMED, v. 9, n. 1, p. 119–139, 2017. REFERÊNCIAS BIBLIOGRÁFICAS 64 CAZELLA, S. C.; NUNES, M.; REATEGUI, E. A ciˆencia da opini˜ao: Estado da arte em sistemas de recomenda¸c˜ao. Andr´e Ponce de Leon F. de Carvalho; Tomasz Kowaltowski..(Org.). Jornada de Atualiza¸c˜ao de Inform´atica-JAI, p. 161–216, 2010. CFA, I. Conhe¸ca as quatro Revolucoes Industriais que moldaram a trajet´oria do mundo. 2019. Dispon´ıvel em: <https://cfa.org.br/as-outras-revolucoes-industriais/>. Acesso em: 3 de Mar¸co de 2022. CHANDRA, A. L. McCulloch-Pitts Neuron — Mankind’s First Mathematical Model Of A Biological Neuron. 2018. Dispon´ıvel em: <towardsdatascience.com/ mcculloch-pitts-model-5fdf65ac5dd1>. Acesso em: 10 de dezembro de 2021. CLUNE, J. et al. On the performance of indirect encoding across the continuum of regularity. IEEE Transactions on Evolutionary Computation, IEEE, v. 15, n. 3, p. 346–367, 2011. CORTES, C.; VAPNIK, V. Support-vector networks. Machine learning, Springer, v. 20, n. 3, p. 273–297, 1995. DASGUPTA, D.; MCGREGOR, D. R. Designing application-specific neural networks using the structured genetic algorithm. p. 87–96, 1992. DASGUPTA, D.; MCGREGOR, D. R. sGA: A structured genetic algorithm. [S.l.]: Citeseer, 1993. DUCOSIM. Ducosim. 2020. Dispon´ıvel em: <https://www.ducosim.nl>. Acesso em: 3 de Mar¸co de 2022. EIBEN, A. E.; SMITH, J. E. et al. Introduction to evolutionary computing. [S.l.]: Springer, 2003. v. 53. EIBEN, J. S. a. A. Introduction to evolutionary computing. Natural Computing Series, Springer-Verlag Berlin Heidelberg, 2015. EIBEN, J. S. A. E. Introduction to Evolutionary Computing. [S.l.]: Springer, 2008. (Natural computing series). ISBN 9783540401841; 3540401849. EXAME, E. E. Pesquisa aponta que 3 em cada 4 brasileiros jogam jogos eletrˆonicos. 2020. Dispon´ıvel em: <https://exame.com/colunistas/esporte-executivo/ pesquisa-aponta-que-3-em-cada-4-brasileiros-jogam-jogos-eletronicos/>. Acesso em: 3 de Mar¸co de 2022. FLOREANO, D.; DURR, P.; MATTIUSSI, C. Neuroevolution: from architectures to learning. Evolutionary Intelligence, v. 1, n. 1, p. 47–62, 2008. FOGEL, D. B. et al. A self-learning evolutionary chess program. Proceedings of the IEEE, IEEE, v. 92, n. 12, p. 1947–1954, 2004. FRAN ¸CA, L. D. R. Neuroevolution of augmenting topologies applied to the detection of cancer in medical images. 2018. REFERÊNCIAS BIBLIOGRÁFICAS 65 GAUCI, J.; STANLEY, K. Generating large-scale neural networks through discovering geometric regularities. In: Proceedings of the 9th annual conference on Genetic and evolutionary computation. [S.l.: s.n.], 2007. p. 997–1004. GOMEZ, F.; MIIKKULAINEN, R. Incremental evolution of complex general behavior. Adaptive Behavior, Sage Publications Sage CA: Thousand Oaks, CA, v. 5, n. 3-4, p. 317–342, 1997. GRAND, S. et al. Creatures: Artificial life autonomous software agents for home entertainment. Proceedings of the first international conference on Autonomous agents, p. 22–29, 01 1997. GRUAU, F. Neural network synthesis using cellular encoding and the genetic algorithm. Citeseer, 1994. HANSEN, N.; OSTERMEIER, A. Completely derandomized self-adaptation in evolution strategies. Evolutionary computation, MIT Press, v. 9, n. 2, p. 159–195, 2001. HAUSKNECHT MATTHEW; LEHMAN, J. M. R. S. P. A neuroevolution approach to general atari game playing. IEEE Transactions on Computational Intelligence and AI in Games, v. 6, p. 355–366, 2014. ISSN 1943-068X,1943-0698. Dispon´ıvel em: <http://doi.org/10.1109/TCIAIG.2013.2294713>. HEIDENREICH, H. NEAT: An Awesome Approach to NeuroEvolution. 2019. Dispon´ıvel em: <https://towardsdatascience.com/ neat-an-awesome-approach-to-neuroevolution-3eca5cc7930f>. Acesso em: 26 de novembro de 2021. HO, T. K. Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition. [S.l.: s.n.], 1995. v. 1, p. 278–282 vol.1. HOLLAND, J. H. Genetic algorithms. Scholarpedia, v. 7, n. 12, p. 1482, 2012. Revision #128222. HUANG, T. Computer Vision: Evolution And Promise. 1996. Dispon´ıvel em: <http: //cds.cern.ch/record/400313>. HURWITZ, J.; KIRSCH, D. Machine Learning. [S.l.]: IBM, 2018. HURWITZ, J.; KIRSCH, D. Machine learning for dummies. IBM Limited Edition, John Wiley & Sons, Inc, v. 75, 2018. IBMCLOUD. Machine Learning. 2020. Dispon´ıvel em: <https://www.ibm.com/cloud/learn/ machine-learning>. Acesso em: 10 de dezembro de 2021. IBMCLOUD. Machine Learning: Unsupervised Learning. 2020. Dispon´ıvel em: <https: //www.ibm.com/cloud/learn/unsupervised-learning>. Acesso em: 10 de dezembro de 2021. IZENMAN, A. J. Introduction to manifold learning. Wiley Interdisciplinary Reviews: Computational Statistics, Wiley Online Library, v. 4, n. 5, p. 439–446, 2012. REFERÊNCIAS BIBLIOGRÁFICAS 66 JALLOV, D.; RISI, S.; TOGELIUS, J. Evocommander: A novel game based on evolving and switching between artificial brains. IEEE Transactions on Computational Intelligence and AI in Games, IEEE, v. 9, n. 2, p. 181–191, 2016. JONG, K. A. D. Evolutionary computation: a unified approach. 1st. ed. The MIT Press, 2002. ISBN 9780262041942,0262041944. Dispon´ıvel em: <http://gen.lib.rus.ec/book/index.php? md5=0e8d3e34b41a8eebf70948d2e116a6ff>. KI, H.-w.; LYU, J.-h.; OH, K.-s. Real-time neuroevolution to imitate a game player. In: PAN, Z. et al. (Ed.). Technologies for E-Learning and Digital Entertainment. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. p. 658–668. ISBN 978-3-540-33424-8. KONGTHON, A. et al. Implementing an online help desk system based on conversational agent. In: Proceedings of the International Conference on Management of Emergent Digital EcoSystems. New York, NY, USA: Association for Computing Machinery, 2009. (MEDES ’09). ISBN 9781605588292. Dispon´ıvel em: <https://doi.org/10.1145/1643823.1643908>. LEHMAN, J.; MIIKKULAINEN, R. Neuroevolution. Scholarpedia, v. 8, n. 6, p. 30977, 2013. Revision #137053. LOCKETT, A. J.; MIIKKULAINEN, R. Evolving opponent models for texas hold’em. In: IEEE. 2008 IEEE Symposium On Computational Intelligence and Games. [S.l.], 2008. p. 31–38. LOPEZ-SAMANIEGO, L. et al. Cognitive rehabilitation based on working brain reflexes using computer games over iPad. In: 2014 Computer Games: AI, Animation, Mobile, Multimedia, Educational and Serious Games (CGAMES). IEEE, 2014. Dispon´ıvel em: <https://doi.org/10.1109\%2Fcgames.2014.6934155>. LUGER, G. Inteligˆencia Artificial: Estruturas e estrat´egias para a solu¸c˜ao de problemas complexos. Bookman, 2004. ISBN 9788536303963. Dispon´ıvel em: <https://books.google.com. br/books?id=ruZNPgAACAAJ>. MAIMON, L. R. O. Data Mining and Knowledge Discovery Handbook. 1. ed. [S.l.]: Springer, 2005. ISBN 9780387244358,0387244352,9780387254654. MANDISCHER, M. Representation and evolution of neural networks. p. 643–649, 1993. MARSLAND, S. Machine learning: an algorithmic perspective. [S.l.]: Chapman and Hall/CRC, 2011. MATOS, E. C. do A.; LIMA, M. A. S. Jogos eletrˆonicos e educa¸c˜ao: Notas sobre a aprendizagem em ambientes interativos. RENOTE, v. 13, n. 1, 2015. MCCULLOCH, W. S.; PITTS, W. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, Springer, v. 5, n. 4, p. 115–133, 1943. MICROSOFT. Visual Studio Code. 2015. Dispon´ıvel em: <https://code.visualstudio.com/>. Acesso em: 3 de Mar¸co de 2022. MICROSOFT. XBOX. 2021. Dispon´ıvel em: <https://www.xbox.com/pt-BR/>. Acesso em: 02 de Dezembro de 2021. REFERÊNCIAS BIBLIOGRÁFICAS 67 MICROSOFT. Xbox Adaptive Controller. 2021. Dispon´ıvel em: <https://www.xbox.com/ en-CA/accessories/controllers/xbox-adaptive-controller>. Acesso em: 02 de Dezembro de 2021. MIRA, J.; SANDOVAL, F. From Natural to Artificial Neural Computation: International Workshop on Artificial Neural Networks, Malaga-Torremolinos, Spain, June 7-9, 1995: Proceedings. [S.l.]: Springer Science & Business Media, 1995. v. 930. MORAL, P. D.; MICLO, L. Branching and interacting particle systems. Approximations of Feynman-Kac formulae with applications to non-linear filtering. S´eminaire de probabilit´es de Strasbourg, Springer - Lecture Notes in Mathematics, v. 34, p. 1–145, 2000. Dispon´ıvel em: <http://archive.numdam.org/item/SPS\ 2000\ \ 34\ \ 1\ 0/>. MORETTIN, L. G. Estat´ıstica B´asica - Probabilidade e Inferˆencia. Pearson Prentice Hall, 2010. (09-09445). ISBN 978-85-7605-370-5. Dispon´ıvel em: <http://gen.lib.rus.ec/book/index. php?md5=f8fcb0e2eb17d30b4e908a6c52a4b9fc>. NAIR, V.; HINTON, G. E. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning. Madison, WI, USA: Omnipress, 2010. (ICML’10), p. 807–814. ISBN 9781605589077. ORDONEZ TIAGO NASCIMENTO; BORGES, F. K. C. S. S. C. C. d. N. H. S. S. L.-S. T. B. Actively station: Effects on global cognition of mature adults and healthy elderly program using eletronic games. Dementia & Neuropsychologia, v. 11, p. 186–197, 2017. ISSN 1980-5764. Dispon´ıvel em: <http://doi.org/10.1590/1980-57642016dn11-020011>. ORLAND, K. How an SNES emulator solved overclocking. 2019. Dispon´ıvel em: <https://arstechnica.com/gaming/2019/08/ blast-processing-in-2019-how-an-snes-emulator-solved-overclocking/>. Acesso em: 3 de Mar¸co de 2022. OTTERLO, M. V.; WIERING, M. Reinforcement learning and markov decision processes. In: Reinforcement learning. [S.l.]: Springer, 2012. p. 3–42. PARKER, M.; BRYANT, B. D. Neuro-visual control in the quake II game engine. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). IEEE, 2008. Dispon´ıvel em: <Parker:2008>. PEDERSEN C.; TOGELIUS, J. Y. G. Modeling player experience for content creation. IEEE Transactions on Computational Intelligence and AI in Games, v. 2, p. 54–67, 2010. ISSN 1943-068X,1943-0698. Dispon´ıvel em: <http://doi.org/10.1109/tciaig.2010.2043950>. PEGI. PEGI game rating. 2020. Dispon´ıvel em: <https://pegi.info/page/how-we-rate-games>. Acesso em: 3 de Mar¸co de 2022. PEREZ-LIEBANA, D. et al. General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms. arXiv, 2018. Dispon´ıvel em: <https://arxiv.org/abs/1802.10363>. REFERÊNCIAS BIBLIOGRÁFICAS 68 PRESS SAUL A. TEUKOLSKY, W. T. V. B. P. F. W. H. Numerical recipes: the art of scientific computing. 3rd ed. ed. [S.l.]: Cambridge University Press, 2007. ISBN 9780511335556,9780521880688,0511335555,0521880688,9780521884075,0521884071,9780521706858,0521706PUJOL, J. C. F.; POLI, R. Evolving the topology and the weights of neural networks using a dual representation. Applied Intelligence, 1998. RADCLIFFE, N. J. Genetic set recombination and its application to neural network topology optimisation. Neural Computing & Applications, Springer, v. 1, n. 1, p. 67–90, 1993. RISI, S. et al. Petalz: Search-based procedural content generation for the casual gamer. IEEE Transactions on Computational Intelligence and AI in Games, v. 8, n. 3, p. 244–255, 2016. RISI, S.; STANLEY, K. O. An Enhanced Hypercube-Based Encoding for Evolving the Placement, Density, and Connectivity of Neurons. Artificial Life, v. 18, n. 4, p. 331–363, 10 2012. ISSN 1064-5462. Dispon´ıvel em: <https://doi.org/10.1162/ARTL\\ a\\ 00071>. RISI, S.; TOGELIUS, J. Neuroevolution in games: State of the art and open challenges. IEEE Transactions on Computational Intelligence and AI in Games, IEEE, v. 9, n. 1, p. 25–41, 2015. RISI, S.; TOGELIUS, J. Neuroevolution in Games: State of the Art and Open Challenges. [S.l.], 2017. v. 9, n. 1, 25-41 p. RONALD, E.; SCHOENAUER, M. Genetic lander: An experiment in accurate neuro-genetic control. Springer-Verlag, p. 452–461, 1994. RUMMERY, G. A.; NIRANJAN, M. On-line Q-learning using connectionist systems. [S.l.]: Citeseer, 1994. v. 37. RUSSELL, S.; NORVIG, P.; CANNY, J. Artificial Intelligence: A Modern Approach. Prentice Hall/Pearson Education, 2003. (Prentice Hall series in artificial intelligence). ISBN 9780137903955. Dispon´ıvel em: <https://books.google.com.br/books? id=KI2WQgAACAAJ>. SCHRUM, J.; MIIKKULAINEN, R. Evolving multimodal behavior with modular neural networks in ms. pac-man. In: Proceedings of the 2014 annual conference on genetic and evolutionary computation. [S.l.: s.n.], 2014. p. 325–332. SIENA, M. C. d. S. et al. O uso de jogos digitais como ferramenta auxiliar no ensino da matem´atica e o prot´otipo do game sinapsis. Universidade Federal de Goi´as, 2018. SOARES, A. Y. et al. Os efeitos da pr´atica de jogos eletrˆonicos ativos na qualidade de vida de pacientes com a doen¸ca de parkinson: uma revis˜ao sistem´atica. Florian´opolis, SC., 2017. SOLARI, G. Daltonismo, acessibilidade e o cara que joga ”Zelda” sem enxergar. 2011. Dispon´ıvel em: <https://tecnologia.uol.com.br/ultnot/2011/04/27/ alem-do-jogo-daltonismo-acessibilidade-zelda.jhtm>. Acesso em: 3 de Mar¸co de 2022. REFERÊNCIAS BIBLIOGRÁFICAS 69 SOUZA, S. F. de. Uma an´alise da tradu¸c˜ao oficial do jogo Life is Strange. 2011. Dispon´ıvel em: <https://monografias.brasilescola.uol.com.br/arte-cultura/ traducao-localizacao-uma-analise-da-traducao-oficial-do-jogo-life-is-strange.htm>. Acesso em: 3 de Mar¸co de 2022. STANLEY, K. Find the Right Version of NEAT for Your Needs. 2015. Dispon´ıvel em: <http://eplex.cs.ucf.edu/neat\ software/\#NEAT>. Acesso em: 27 de novembro de 2021. STANLEY, K. O. Compositional pattern producing networks: A novel abstraction of development. Genetic programming and evolvable machines, Springer, v. 8, n. 2, p. 131–162, 2007. STANLEY, K. O.; BRYANT, B. D.; MIIKKULAINEN, R. Real-time neuroevolution in the nero video game. IEEE transactions on evolutionary computation, IEEE, v. 9, n. 6, p. 653–668, 2005. STANLEY, K. O.; MIIKKULAINEN, R. Evolving neural networks through augmenting topologies. Evolutionary Computation, v. 10, n. 2, p. 99–127, 2002. Dispon´ıvel em: <http://nn.cs.utexas.edu/?stanley:ec02>. STANLEY, K. O.; MIIKKULAINEN, R. Evolving a roving eye for go. In: SPRINGER. Genetic and Evolutionary Computation Conference. [S.l.], 2004. p. 1226–1238. TASVIDEOS. https://tasvideos.org/Bizhawk. 2017. Dispon´ıvel em: <https://tasvideos.org/ Bizhawk>. Acesso em: 3 de Mar¸co de 2022. TOLLES, J.; MEURER, W. J. Logistic Regression: Relating Patient Characteristics to Outcomes. JAMA, v. 316, n. 5, p. 533–534, 08 2016. ISSN 0098-7484. Dispon´ıvel em: <https://doi.org/10.1001/jama.2016.7653>. UNIVERSITY, I. S. Video game ratings work, if you use them. 2017. Dispon´ıvel em: <https://www.sciencedaily.com/releases/2017/01/170125145805.htm>. Acesso em: 3 de Mar¸co de 2022. WANG, W. Machine Audition: Principles, Algorithms and Systems: Principles, Algorithms and Systems. [S.l.]: IGI Global, 2010. WATKINS, C. J.; DAYAN, P. Q-learning. Machine learning, Springer, v. 8, n. 3, p. 279–292, 1992. WEBB, G. I.; KEOGH, E.; MIIKKULAINEN, R. Na¨ıve bayes. Encyclopedia of machine learning, v. 15, p. 713–714, 2010. WIKIPEDIA. Sonic Adventure. 2021. Dispon´ıvel em: <https://en.wikipedia.org/wiki/Sonic\ Adventure>. Acesso em: 02 de Dezembro de 2021. WIKIPEDIA. Super Mario Bros. 2021. Dispon´ıvel em: <https://en.wikipedia.org/wiki/ Super\ Mario\ Bros.> Acesso em: 02 de Dezembro de 2021. WONG, A. L.; HAITH, A. M.; KRAKAUER, J. W. Motor planning. The Neuroscientist, Sage Publications Sage CA: Los Angeles, CA, v. 21, n. 4, p. 385–398, 2015. REFERÊNCIAS BIBLIOGRÁFICAS 70 YAN, X. G. S. X. Linear Regression Analysis: Theory and Computing. 1. ed. [S.l.]: World Scientific Publishing Company, 2009. ISBN 9789812834102,9812834109. ZHU, D. How I Built an Intelligent Agent to Play Flappy Bird. 2020. Dispon´ıvel em: <https: //medium.com/analytics-vidhya/how-i-built-an-ai-to-play-flappy-bird-81b672b66521>. Acesso em: 13 de dezembro de 2021.pt_BR
dc.subject.cnpqEngenharia de Softwarept_BR
dc.subject.cnpqLinguagens de Programaçãopt_BR
dc.publisher.initialsUEApt_BR
Aparece nas coleções:EST - Trabalho de Conclusão de Curso Graduação

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
Neuro-evolução aplicada na conclusão de jogos de plataforma.pdf1,54 MBAdobe PDFVisualizar/Abrir


Os itens no repositório estão protegidos por copyright, com todos os direitos reservados, salvo quando é indicado o contrário.