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Exploring representation in evolutionary level design /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	006.3823
書名/作者:	Exploring representation in evolutionary level design // Daniel Ashlock.
作者:	Ashlock, Daniel,
出版者:	[San Rafael, California] : : Morgan & Claypool,, 2018.
面頁冊數:	1 PDF (xiii, 141 pages) : : illustrations.
附註:	Part of: Synthesis digital library of engineering and computer science.
標題:	Evolutionary computation.
標題:	Computer games - Design.
ISBN:	9781681733319
書目註:	Includes bibliographical references (pages 133-139).
內容註:	1. Introduction -- 1.1 Evolutionary computation -- 1.2 Elements of fitness for level design -- 1.3 Obscured mazes: a simple example -- 1.3.1 Chess mazes -- 1.3.2 Chromatic mazes -- 1.3.3 Key maps and alternate views -- 1.4 Conclusions --
摘要、提要註:	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 designer-supplied 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.
電子資源:	https://ieeexplore.ieee.org/servlet/opac?bknumber=8363144

Exploring representation in evolutionary level design /
Ashlock, Daniel,

Exploring representation in evolutionary level design /Daniel Ashlock. - [San Rafael, California] :Morgan & Claypool,2018. - 1 PDF (xiii, 141 pages) :illustrations. - Synthesis lectures on games and computational intelligence,# 32573-6493 ;. - Synthesis digital library of engineering and computer science..

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 133-139).

1. Introduction -- 1.1 Evolutionary computation -- 1.2 Elements of fitness for level design -- 1.3 Obscured mazes: a simple example -- 1.3.1 Chess mazes -- 1.3.2 Chromatic mazes -- 1.3.3 Key maps and alternate views -- 1.4 Conclusions --

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Compendex

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 designer-supplied 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.

Mode of access: World Wide Web.

ISBN: 9781681733319

Standard No.: 10.2200/S00840ED1V01Y201803GCI003doiSubjects--Topical Terms:

404545
Evolutionary computation.
Subjects--Index Terms:

content generationIndex Terms--Genre/Form:

336502
Electronic books.

LC Class. No.: TA347.E96 / A833 2018

Dewey Class. No.: 006.3823

Exploring representation in evolutionary level design /
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100 1 $a Ashlock, Daniel, $e author. $3 729069
245 1 0 $a Exploring representation in evolutionary level design / $c Daniel Ashlock.
260 1 $a [San Rafael, California] : $b Morgan & Claypool, $c 2018.
264 1 $a [San Rafael, California] : $b Morgan & Claypool, $c 2018.
300 $a 1 PDF (xiii, 141 pages) : $b illustrations.
336 $a text $2 rdacontent
337 $a electronic $2 isbdmedia
338 $a online resource $2 rdacarrier
490 1 $a Synthesis lectures on games and computational intelligence, $x 2573-6493 ; $v # 3
500 $a Part of: Synthesis digital library of engineering and computer science.
504 $a Includes bibliographical references (pages 133-139).
505 0 $a 1. Introduction -- 1.1 Evolutionary computation -- 1.2 Elements of fitness for level design -- 1.3 Obscured mazes: a simple example -- 1.3.1 Chess mazes -- 1.3.2 Chromatic mazes -- 1.3.3 Key maps and alternate views -- 1.4 Conclusions --
505 8 $a 2. Contrasting representations for maze generation -- 2.1 Details of the binary direct representation -- 2.2 Details of the chromatic representation -- 2.3 Details of the positive, indirect representation -- 2.4 Details of the negative, indirect representation -- 2.5 Fitness function design -- 2.5.1 Definitions -- 2.5.2 Fitness functions -- 2.6 Design of experiments -- 2.6.1 Initial experiments -- 2.6.2 Experiments with culs-de-sac -- 2.6.3 Changing the board size -- 2.6.4 Experiments with the chromatic representation -- 2.6.5 Verification of sparse initialization and crossover -- 2.7 Results and discussion for maze generation -- 2.7.1 Experiments with culs-de-sac -- 2.7.2 Experiments with different board sizes -- 2.7.3 Sparse initialization and choice of crossover operator -- 2.7.4 Algorithm speed -- 2.7.5 Fitness landscapes and sparse initialization -- 2.7.6 Discussion for maze generation -- 2.7.7 Breaking out of two dimensions --
505 8 $a 3. Dual mazes -- 3.1 Representations for dual maze generation -- 3.2 Details of the generative representation -- 3.2.1 Details of the direct representation -- 3.2.2 Fitness function specification -- 3.3 Experimental design -- 3.4 Results and discussion for dual mazes -- 3.5 Conclusions and next steps for dual mazes -- 3.5.1 Additional fitness elements -- 3.5.2 Tool development -- 3.5.3 Visibility and lines of sight -- 3.5.4 Terrain types --
505 8 $a 4. Terrain maps -- 4.1 Midpoint L-systems -- 4.1.1 The representation for midpoint L-systems -- 4.1.2 Multiscale landforms -- 4.2 Landscape automata: another representation for height maps -- 4.2.1 Defining landscape automata -- 4.2.2 Experiments with landscape automata -- 4.2.3 Results and discussion for landscape automata -- 4.2.4 Qualitative diversity -- 4.2.5 Conclusions and next steps for landscape automata -- 4.3 Morphing and smoothing of height maps --
505 8 $a 5. Cellular automata based maps -- 5.1 Fashion-based cellular automata -- 5.1.1 Design of experiments -- 5.1.2 Results and discussion for cellular automata level creation -- 5.1.3 Discussion for cellular automata level design -- 5.1.4 Using an optimizer for non-optimization goals -- 5.2 Generalizing fitness and morphing -- 5.2.1 Generalizing the fitness function to control open space -- 5.2.2 Return of dynamic programming based fitness -- 5.2.3 Morphing between rules -- 5.2.4 More general application of morphing: re-evolution --
505 8 $a 6. Decomposition, tiling, and assembly -- 6.1 More maps than you could ever use -- 6.1.1 Details of tile production -- 6.1.2 Enumerating maps and exploiting tile symmetries -- 6.2 Required content -- 6.2.1 The fitness function for required content tiles -- 6.2.2 Results of the tile creation experiments -- 6.3 Creating an integrated adventure: goblins attack the village -- 6.3.1 System design for FRPG module creation -- 6.3.2 The level evolver -- 6.3.3 Identifying and connecting rooms -- 6.3.4 Populating the dungeon -- 6.3.5 Results for FRPG module creation -- 6.3.6 Conclusions and next steps for FRPG module generation -- 6.3.7 Decorations: monsters, treasure, and traps -- 6.3.8 History, context, and story --
505 8 $a Bibliography -- Author's biography.
506 $a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 $a Compendex
510 0 $a INSPEC
510 0 $a Google scholar
510 0 $a Google book search
520 3 $a 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 designer-supplied 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.
530 $a Also available in print.
538 $a Mode of access: World Wide Web.
588 $a Title from PDF title page (viewed on May 25, 2018).
650 0 $a Evolutionary computation. $3 404545
650 0 $a Computer games $x Design. $3 403485
653 $a content generation
653 $a procedural content generation
653 $a evolutionary computation
653 $a level design
653 $a terrain maps
653 $a automatic design
653 $a cellular automata
655 0 $a Electronic books. $2 local $3 336502
776 0 8 $i Print version: $z 9781681733302 $z 9781681733326
830 0 $a Synthesis digital library of engineering and computer science. $3 461208
830 0 $a Synthesis lectures on games and computational intelligence ; $v # 3. $x 2573-6493 $3 729070
856 4 2 $3 Abstract with links to resource $u https://ieeexplore.ieee.org/servlet/opac?bknumber=8363144