Abstract
Generating fiction environments for a multi-agent system optimized by genetic algorithms (with some specific requirements related to the desirable plots), presents two main problems: first it is impossible to know in advance the optimal value for the particular designed fitness function, and at the same time, it creates a vast search space for the parameters that it needs. The purpose of this paper is to define a methodology to find the best parameter values for both, the evolutionary algorithm, and the own fictional world configuration. This design includes running, to completion, a world simulation represented as a chromosome, and assigning a fitness to it, thus composing a very complex fitness landscape.
In order to optimize the resources allocated to evolution and to have some guarantees that the final result will be close to the optimum, we systematically analyze a set of possible values of the most relevant parameters, obtaining a set of generic rules. These rules, when applied to the plot requisites, and thus, to the fitness function, will lead to a reduced range of parameter values that will help the storyteller to create optimal worlds with a reduced computation budget.
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Which can be downloaded from Github at https://github.com/raiben/made under a GNU/GPL V3 License.
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Acknowledgements
This work has been supported in part by SIPESCA (Programa Operativo FEDER de Andalucía 2007–2013), TIN2011-28627-C04-02 (Spanish Ministry of Economy and Competitivity), SPIP2014-01437 (Dirección General de Tráfico), PRY142/14 (Fundación Pública Andaluza Centro de Estudios Andaluces en la IX Convocatoria de Proyectos de Investigación) and PYR-2014-17 GENIL project (CEI-BIOTIC Granada).
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García-Ortega, R.H., García-Sánchez, P., Merelo, J.J., Arenas, M.G., Castillo, P.A., Mora, A.M. (2015). How the World Was MADE: Parametrization of Evolved Agent-Based Models for Backstory Generation. In: Mora, A., Squillero, G. (eds) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science(), vol 9028. Springer, Cham. https://doi.org/10.1007/978-3-319-16549-3_36
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DOI: https://doi.org/10.1007/978-3-319-16549-3_36
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