000 03634nam a22005655i 4500
001 978-3-031-02120-6
003 DE-He213
005 20240730163812.0
007 cr nn 008mamaa
008 220601s2018 sz | s |||| 0|eng d
020 _a9783031021206
_9978-3-031-02120-6
024 7 _a10.1007/978-3-031-02120-6
_2doi
050 4 _aQA1-939
072 7 _aPB
_2bicssc
072 7 _aMAT000000
_2bisacsh
072 7 _aPB
_2thema
082 0 4 _a510
_223
100 1 _aAshlock, Daniel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980604
245 1 0 _aExploring Representation in Evolutionary Level Design
_h[electronic resource] /
_cby Daniel Ashlock.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIV, 141 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aMathematics.
_911584
650 0 _aEngineering.
_99405
650 0 _aComputational intelligence.
_97716
650 0 _aPopular Culture.
_978660
650 0 _aArtificial intelligence.
_93407
650 1 4 _aMathematics.
_911584
650 2 4 _aTechnology and Engineering.
_980605
650 2 4 _aComputational Intelligence.
_97716
650 2 4 _aPopular Culture.
_978660
650 2 4 _aArtificial Intelligence.
_93407
710 2 _aSpringerLink (Online service)
_980606
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031001697
776 0 8 _iPrinted edition:
_z9783031009921
776 0 8 _iPrinted edition:
_z9783031032486
830 0 _aSynthesis Lectures on Games and Computational Intelligence,
_x2573-6493
_980607
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02120-6
912 _aZDB-2-SXSC
942 _cEBK
999 _c84993
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