Learning How to Reinterpret Creative Problems
Inhaltsverzeichnis
Reference
Kazjon Grace, John Gero and Rob Saunders: Learning How to Reinterpret Creative Problems. In: Computational Creativity 2013 ICCC 2013, 113-117.
DOI
Abstract
This paper discusses a method, implemented in the do- main of computational association, by which computa- tional creative systems could learn from their previous experiences and apply them to influence their future be- haviour, even on creative problems that differ signifi- cantly from those encountered before. The approach is based on learning ways that problems can be reinter- preted. These interpretations may then be applicable to other problems in ways that specific solutions or object knowledge may not. We demonstrate a simple proof-of- concept of this approach in the domain of simple visual association, and discuss how and why this behaviour could be integrated into other creative systems.
Extended Abstract
Bibtex
@inproceedings{
author = {Kazjon Grace, John Gero and Rob Saunders},
title = {Learning How to Reinterpret Creative Problems},
editor = {Simon Colton, Dan Ventura, Nada Lavrac, Michael Cook},
booktitle = {Proceedings of the Fourth International Conference on Computational Creativity},
series = {ICCC2013},
year = {2013},
month = {Jun},
location = {Sydney, New South Wales, Australia},
pages = {113-117},
url = {http://www.computationalcreativity.net/iccc2013/download/iccc2013-grace-gero-saunders.pdf, http://de.evo-art.org/index.php?title=Learning_How_to_Reinterpret_Creative_Problems },
publisher = {International Association for Computational Creativity},
keywords = {computational, creativity},
}
Used References
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Links
Full Text
http://www.computationalcreativity.net/iccc2013/download/iccc2013-grace-gero-saunders.pdf
