Exploring Representation in Evolutionary Level Design

Ashlock, Daniel

ISBN 10: 3031009924 / ISBN 13: 9783031009921

Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2018

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 present.

Seller Inventory # 608129245

Bibliographic Details

• Title: Exploring Representation in Evolutionary Level Design
• Publisher: Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer
• Publication Date: 2018
• Language: English
• ISBN 10: 3031009924
• ISBN 13: 9783031009921
• Binding: Soft cover
• Condition: New
• Seller catalogs:
  • Mathematik/Naturwissenschaften/Technik/Medizin

About this title

Synopsis

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.

About the Author

Dr. Daniel Ashlock is a professor of mathematics at the University of Guelph in Ontario, Canada. Dr. Ashlock received his Ph.D. in mathematics from Caltech with a focus in algebraic combinatorics. He was employed at Iowa State University before moving to Canada. Dr. Ashlock works on representation issues in evolutionary computation including games, optimization, bioinformatics, and theoretical biology. He holds the Bioinformatics Chair in the Department of Mathematics and Statistics at Guelph and serves on the editorial board of the IEEE Transactions on Evolutionary Computation, the IEEE Transactions on Games, The IEEE/ACM Transactions on Bioinformatics and Computational Biology, Biosystems, and Game and Puzzle Design. Dr. Ashlock serves on the IEEE Computational Intelligence Societies technical committees on games and bioinformatics and biomedical engineering.