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dc.contributor.authorSalem, Mohammed
dc.contributor.authorMora, Antonio M (1)
dc.contributor.authorMerelo, Juan Julián
dc.contributor.authorGarcía-Sánchez, Pablo
dc.date2018
dc.date.accessioned2020-09-02T10:45:22Z
dc.date.available2020-09-02T10:45:22Z
dc.identifier.citationSalem M., Mora A.M., Merelo Guervós J.J., García-Sánchez P. (2018) Applying Genetic Algorithms for the Improvement of an Autonomous Fuzzy Driver for Simulated Car Racing. In: Medina J., Ojeda-Aciego M., Verdegay J., Perfilieva I., Bouchon-Meunier B., Yager R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2018. Communications in Computer and Information Science, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-91479-4_20es_ES
dc.identifier.isbn9783319914787
dc.identifier.issn1865-0929
dc.identifier.urihttps://reunir.unir.net/handle/123456789/10478
dc.descriptionPonencia de la conferencia "17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018; Cadiz; Spain; 11 June 2018 through 15 June 2018"es_ES
dc.description.abstractGames offer a suitable testbed where new methodologies and algorithms can be tested in a near-real life environment. For example, in a car driving game, using transfer learning or other techniques results can be generalized to autonomous driving environments. In this work, we use evolutionary algorithms to optimize a fuzzy autonomous driver for the open simulated car racing game TORCS. The Genetic Algorithm applied improves the fuzzy systems to set an optimal target speed as well as the instantaneous steering angle during the race. Thus, the approach offer an automatic way to define the membership functions, instead of a manual or hill-climbing descent method. However, the main issue with this kind of algorithms is to define a proper fitness function that best delivers the obtained result, which is eventually to win as many races as possible. In this paper we define two different evaluation functions, and prove that fine-tuning the controller via evolutionary algorithms robustly finds good results and that, in many cases, they are able to play very competitively against other published results, with a more relying approach that needs very few parameters to tune. The optimized fuzzy-controllers (one per fitness) yield a very good performance, mainly in tracks that have many turning points, which are, in turn, the most difficult for any autonomous agent. Experimental results show that txshe enhanced controllers are very competitive with respect to the embedded TORCS drivers, and much more efficient in driving than the original fuzzy-controller.es_ES
dc.language.isoenges_ES
dc.publisherCommunications in Computer and Information Sciencees_ES
dc.relation.ispartofseries;vol. 855
dc.relation.urihttps://link.springer.com/chapter/10.1007%2F978-3-319-91479-4_20es_ES
dc.rightsrestrictedAccesses_ES
dc.subjectfuzzy controlleres_ES
dc.subjectgenetic algorithmses_ES
dc.subjectoptimizationes_ES
dc.subjectsteering controles_ES
dc.subjectTORCSes_ES
dc.subjectvideogameses_ES
dc.subjectScopus(2)es_ES
dc.subjectWOS(2)es_ES
dc.titleApplying genetic algorithms for the improvement of an autonomous fuzzy driver for simulated car racinges_ES
dc.typebookPartes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1007/978-3-319-91479-4_20


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