Intelligent opponent behavior helps make video
games interesting to human players. Evolutionary computation
can discover such behavior, especially when the game consists of
a single task. However, multitask domains, in which separate
tasks within the domain each have their own dynamics and
objectives, can be challenging for evolution. This paper proposes
two methods for meeting this challenge by evolving neural
networks: 1) Multitask Learning provides a network with
distinct outputs per task, thus evolving a separate policy for
each task, and 2) Mode Mutation provides a means to evolve
new output modes, as well as a way to select which mode
to use at each moment. Multitask Learning assumes agents
know which task they are currently facing; if such information
is available and accurate, this approach works very well, as
demonstrated in the Front/Back Ramming game of this paper.
In contrast, Mode Mutation discovers an appropriate task
division on its own, which may in some cases be even more
powerful than a human-speciļ¬ed task division, as shown in the
Predator/Prey game of this paper. These results demonstrate the
importance of both Multitask Learning and Mode Mutation for
learning intelligent behavior in complex games.
[Winner of the Best Paper award at CIG'11]
[An expanded version was published as a journal article
here]