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2016 | OriginalPaper | Buchkapitel

Dangerousness Metric for Gene Regulated Car Driving

verfasst von : Sylvain Cussat-Blanc, Jean Disset, Stéphane Sanchez

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer International Publishing

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Abstract

In this paper, we show how a dangerousness metric can be used to modify the input of a gene regulatory network when plugged to a virtual car. In the context of the 2015 Simulated Car Racing Championship organized during GECCO 2015, we have developed a new cartography methodology able to inform the controller of the car about the incoming complexity of the track: turns (slipperiness, angle, etc.) and bumps. We show how this dangerousness metric improves the results of our controller and outperforms other approaches on the tracks used in the competition.

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Vorheriges Kapitel The Story of Their Lives: Massive Procedural Generation of Heroes’ Journeys Using Evolved Agent-Based Models and Logical Reasoning

Nächstes Kapitel Using Isovists to Evolve Terrains with Gameplay Elements

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The source code and a short description of these approaches are available on the competition website: http://cs.adelaide.edu.au/~optlog/SCR2015/.

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Titel: Dangerousness Metric for Gene Regulated Car Driving
verfasst von: Sylvain Cussat-Blanc
Jean Disset
Stéphane Sanchez
Verlag: Springer International Publishing
Buch: Applications of Evolutionary Computation
Print ISBN: 978-3-319-31203-3

Electronic ISBN: 978-3-319-31204-0

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-31204-0_40

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