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007			cr nn 008mamaa
008			220601s2018 sz | s |||| 0|eng d
020			_a9783031021206
024	7		_a10.1007/978-3-031-02120-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA347.E96 _b2018 EB
100	1		_aAshlock, Daniel _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686297
245	1	0	_aExploring Representation in Evolutionary Level Design _cby Daniel Ashlock
250			_a1st edition 2018
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XIV, 141 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Games and Computational Intelligence _x2573-6493
505	0		_aPreface -- Acknowledgments -- Introduction -- Contrasting Representations for Maze Generation -- Dual Mazes -- Terrain Maps -- Cellular Automata Based Maps -- Decomposition, Tiling, and Assembly -- Bibliography -- Author's Biography.
520			_aAutomatic 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.
988			_aSynthesis Collection of Technology_2018
650		7	_2embne _9141365 _aVideojuegos _xDiseño
650		7	_2embne _9667195 _aComputación evolutiva
776	0	8	_iPrinted edition: _z9783031001697
776	0	8	_iPrinted edition: _z9783031009921
776	0	8	_iPrinted edition: _z9783031032486
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02120-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI