Constructing Complex NPC Behavior via Multi-Objective Neuroevolution

This is an Accepted Manuscript of an article published in Proceedings of the Fourth Artificial Intelligence and Interactive Digital
Entertainment Conference (AIIDE 2008), pp. 108-113, Stanford, California 2008.

Schrum, Jacob

Miikkulainen, Risto

2016-12-09

2016-12-09

2008

In Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2008), pp. 108-113, Stanford, California 2008.

https://collections.southwestern.edu/s/suscholar/item/220

It is difficult to discover effective behavior for NPCs automatically.
For instance, evolutionary methods can learn sophisticated
behaviors based on a single objective, but realistic
game playing requires different behaviors at different
times. Such complex behavior is difficult to achieve. What
is needed are multi-objective methods that reward different
behaviors separately, and allow them to be combined to produce
multi-modal behavior. While such methods exist, they
have not yet been applied to generating multi-modal behavior
for NPCs. This paper presents such an application: In a domain
with noisy evaluations and contradictory fitness objectives,
evolution based on a scalar fitness function is inferior to
multi-objective optimization. The multi-objective approach
produces agents that excel at the task and develop complex,
interesting behaviors.

English

Game Development

Multi-objective Optimization

Multi-Objective Neuroevolution

Complex NPC Behavior

Article

Faculty Scholarship