Neuroevolution of content layout in the PCG: Angry bots video game

Raffe, W, Zambetta, F and Li, X 2013, 'Neuroevolution of content layout in the PCG: Angry bots video game', in C. A. Coello Coello (ed.) Proceedings of 2013 IEEE Congress on Evolutionary Computation, Cancún, México, 20-23 June 2013, pp. 673-680.


Document type: Conference Paper
Collection: Conference Papers

Title Neuroevolution of content layout in the PCG: Angry bots video game
Author(s) Raffe, W
Zambetta, F
Li, X
Year 2013
Conference name 2013 IEEE Congress on Evolutionary Computation
Conference location Cancún, México
Conference dates 20-23 June 2013
Proceedings title Proceedings of 2013 IEEE Congress on Evolutionary Computation
Editor(s) C. A. Coello Coello
Publisher IEEE
Place of publication Piscataway, USA
Start page 673
End page 680
Total pages 8
Abstract This paper demonstrates an approach to arranging content within maps of an action-shooter game. Content here refers to any virtual entity that a player will interact with during game-play, including enemies and pick-ups. The content layout for a map is indirectly represented by a Compositional Pattern-Producing Networks (CPPN), which are evolved through the Neuroevolution of Augmenting Topologies (NEAT) algorithm. This representation is utilized within a complete procedural map generation system in the game PCG: Angry Bots. In this game, after a player has experienced a map, a recommender system is used to capture their feedback and construct a player model to evaluate future generations of CPPNs. The result is a content layout scheme that is optimized to the preferences and skill of an individual player. We provide a series of case studies that demonstrate the system as it is being used by various types of players.
Subjects Neural, Evolutionary and Fuzzy Computation
Virtual Reality and Related Simulation
Keyword(s) CPPN NEAT algorithm PCG action-shooter game angry bots video game complete procedural map generation system compositional pattern-producing networks content layout neuroevolution enemies game-play neuroevolution-of-augmenting topologies algorithm pick-ups recommender system virtual entity
DOI - identifier 10.1109/CEC.2013.6557633
Copyright notice © 2013 IEEE
ISBN 9781479904532
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