Imitating human playing styles in Super Mario Bros
Yannakakis, Georgios N.
- Publisher: Elsevier Ltd.
Human-computer interaction | Artificial intelligence | Genetic engineering | Interactive computer graphics | Video games
acm: ComputingMilieux_PERSONALCOMPUTING | GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries) | ComputingMethodologies_DOCUMENTANDTEXTPROCESSING | ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION | ComputingMilieux_MISCELLANEOUS
We describe and compare several methods for generating game character
controllers that mimic the playing style of a particular human player, or of a
population of human players, across video game levels. Similarity in playing
style is measured through an evaluation framework, that compares the play
trace of one or several human players with the punctuated play trace of an
AI player. The methods that are compared are either hand-coded, direct
(based on supervised learning) or indirect (based on maximising a similarity
measure). We find that a method based on neuroevolution performs best
both in terms of the instrumental similarity measure and in phenomenological
evaluation by human spectators. A version of the classic platform game
“Super Mario Bros” is used as the testbed game in this study but the methods
are applicable to other games that are based on character movement in space.