Abstract
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.
Publication Date
2017-01-01
Publication Title
Proceedings of AISB Annual Convention 2017
First Page
341
Last Page
343
Organisational Unit
School of Engineering, Computing and Mathematics
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Recommended Citation
Gaudl, S. (2017) 'A genetic programming framework for 2D Platform AI', Proceedings of AISB Annual Convention 2017, , pp. 341-343. Retrieved from https://pearl.plymouth.ac.uk/secam-research/1093