Robust player imitation using multiobjective evolution
Proceedings of the Congress on Evolutionary Computation (CEC-09), (2009)DOI: 10.1109/CEC.2009.4983007
Abstract
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.
Description
diverse cognitive systems bib
