There currently exists a wide range of techniques to model and evolve
artificial players for games. Existing techniques range from black box neural
networks to entirely hand-designed solutions. In this paper, we demonstrate the
feasibility of a genetic programming framework using human controller input to
derive meaningful artificial players which can, later on, be optimised by hand.
The current state of the art in game character design relies heavily on human
designers to manually create and edit scripts and rules for game characters. To
address this manual editing bottleneck, current computational intelligence
techniques approach the issue with fully autonomous character generators,
replacing most of the design process using black box solutions such as neural
networks or the like. Our GP approach to this problem creates character
controllers which can be further authored and developed by a designer it also
offers designers to included their play style without the need to use a
programming language. This keeps the designer in the loop while reducing
repetitive manual labour. Our system also provides insights into how players
express themselves in games and into deriving appropriate models for
representing those insights. We present our framework, supporting findings and
open challenges.

通过遗传编程框架，结合人类控制器的输入数据，创造出有意义的人工玩家，解决现有游戏人物设计中手动编写脚本和规则的瓶颈问题，并提供洞察玩家在游戏中的表现以及相应的模型。