Abstract
Creating the behavior for non-player characters in video games is a complex task that requires the collaboration among programmers and game designers.
Usually game designers are only allowed to change certain parameters of the behavior, while programmers write new code whenever the behavior intended by designers cannot be achieved by just parameter tweaking. This becomes a time-consuming process that requires several iterations of designers testing the solution provided by programmers, followed by additional changes in the requirements that programmers must again re-implement.
In this paper, we present an approach for creating the behavior of non-player characters in video games that gives more power to the game designer by combining program by demonstration and behavior trees. Our approach is able to build some parts of a behavior tree with the observed data in a previous training phase.
Supported by the Spanish Ministry of Science and Education (TIN2014-55006-R).
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Sagredo-Olivenza, I., Gómez-Martín, P.P., Gómez-Martín, M.A., González-Calero, P.A. (2017). Combining Neural Networks for Controlling Non-player Characters in Games. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_59
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