- author = "Swen E. Gaudl",
- title = "A Genetic Programming Framework for {2D} Platform {AI}",
- booktitle = "AISB Symposium on AI \& Games",
- year = "2017",
- editor = "Joanna Bryson and Marina {De Vos} and Julian Padget",
- pages = "341--343",
- publisher_address = "UK",
- month = "21 " # apr,
- organisation = "Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB)",
- keywords = "genetic algorithms, genetic programming, BPGP, Game AI, Agent Design, Platformer, AISB, JGAP, platformerAI, symbolic learning, evolved agent, decision tree",
-
URL = "
https://arxiv.org/pdf/1803.01648",
-
URL = "https://groups.google.com/g/eucog-general-news/c/YlzEWb90SyY?pli=1",
-
URL = "https://scholar.google.com/citations?view_op=view_citation&citation_for_view=1P32b6gAAAAJ:B3FOqHPlNUQC",
- size = "3 pages",
- 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",
-
notes = "Oct 2026 cite AISB 2017 paper rather than ArXiv
1803.01648
JGAP Gamalyzer http://www.platformersai.com
Part of AISB 2017 Bath UK https://cris.brighton.ac.uk/ws/portalfiles/portal/452820/conference-edition-proceedings.pdf",
