Exploring Representation in Evolutionary Level Design / by Daniel Ashlock
By: Ashlock, Daniel, autor
Material type:
E-bookSeries: (Synthesis Lectures on Games and Computational Intelligence, 2573-6493).Publisher: Cham : Springer International Publishing, 2018Edition: 1st edition 2018.Description: 1 recurso en línea (XIV, 141 páginas).ISBN: 9783031021206.Subject: Videojuegos -- Diseño
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|
LIBRO-E NO PRÉSTAMO
|
Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | TA347.E96 2018 EB (Browse shelf(Opens below)) | Acceso electrónico | eBook.01112548 |
Browsing Madrid Digital shelves, Shelving location: Acceso Electrónico (UEM) Close shelf browser (Hides shelf browser)
| TA347 .D45 2020 EB Almost Periodicity, Chaos, and Asymptotic Equivalence | TA347.D5 S566 2017 EB Dimensional analysis for engineers | TA347.D5 Z648 2017 EB Dimensional analysis beyond the pi theorem | TA347.E96 2018 EB Exploring Representation in Evolutionary Level Design | TA347.E96 2019 EB Evolutionary Computation and Complex Networks | TA347 .E96 2021 EB Evolutionary computation in combinatorial optimization : 21st European Conference, EvoCOP 2021, Held as Part of EvoStar 2021, Virtual Event, April 7-9, 2021, Proceedings | TA347.E96 2021 EB Applications of Evolutionary Computation : 24th International Conference, EvoApplications 2021, Held as Part of EvoStar 2021, Virtual Event, April 7-9, 2021, Proceedings |
Preface -- Acknowledgments -- Introduction -- Contrasting Representations for Maze Generation -- Dual Mazes -- Terrain Maps -- Cellular Automata Based Maps -- Decomposition, Tiling, and Assembly -- Bibliography -- Author's Biography.
Automatic content generation is the production of content for games, web pages, or other purposes by procedural means. Search-based automatic content generation employs search-based algorithms to accomplish automatic content generation. This book presents a number of different techniques for search-based automatic content generation where the search algorithm is an evolutionary algorithm. The chapters treat puzzle design, the creation of small maps or mazes, the use of L-systems and a generalization of L-system to create terrain maps, the use of cellular automata to create maps, and, finally, the decomposition of the design problem for large, complex maps culminating in the creation of a map for a fantasy game module with designersupplied content and tactical features. The evolutionary algorithms used for the different types of content are generic and similar, with the exception of the novel sparse initialization technique are presented in Chapter 2. The points where the content generation systems vary are in the design of their fitness functions and in the way the space of objects being searched is represented. A large variety of different fitness functions are designed and explained, and similarly radically different representations are applied to the design of digital objects all of which are, essentially, maps for use in games.
There are no comments on this title.