The problem of how to create NPC AI for videogames that believably imitates particular human players is addressed. Previous approaches to learning player behaviour is found to either not generalize well to new environments and noisy perceptions, or to not reproduce human behaviour in sufficient detail. It is proposed that better solutions to this problem can be built on multiobjective evolutionary algorithms, with objectives relating both to traditional progress-based fitness (playing the game well) and similarity to recorded human behaviour (behaving like the recorded player). This idea is explored in the context of a modern racing game.© 2009 IEEE.
|Original language||English (US)|
|Title of host publication||2009 IEEE Congress on Evolutionary Computation, CEC 2009|
|Number of pages||8|
|State||Published - Nov 25 2009|