Crowd-sourced AI authoring with ENIGMA

Michael Kriegel, Ruth Aylett

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

ENIGMA is an experimental platform for collaborative authoring of the behaviour of autonomous virtual characters in interactive narrative applications. The main objective of this system is to overcome the bottleneck of knowledge acquisition that exists in generative storytelling systems through a combination of crowd-sourcing and machine learning. While the authoring front-end of the application is used to create short example stories set in a specific story domain, the server side of the application collects many of those stories and derives behaviour models for autonomous virtual characters such as formal planning operator descriptions from them. A mixed initiative mode increases coherence by feeding already learnt character behaviour back into the client. © 2010 Springer-Verlag.

Original languageEnglish
Title of host publicationInteractive Storytelling - Third Joint Conference on Interactive Digital Storytelling, ICIDS 2010, Proceedings
Pages275-278
Number of pages4
Volume6432 LNCS
DOIs
Publication statusPublished - 2010
Event3rd Joint Conference on Interactive Digital Storytelling - Edinburgh, United Kingdom
Duration: 1 Nov 20103 Nov 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6432 LNCS
ISSN (Print)0302-9743

Conference

Conference3rd Joint Conference on Interactive Digital Storytelling
Abbreviated titleICIDS 2010
CountryUnited Kingdom
CityEdinburgh
Period1/11/103/11/10

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  • Cite this

    Kriegel, M., & Aylett, R. (2010). Crowd-sourced AI authoring with ENIGMA. In Interactive Storytelling - Third Joint Conference on Interactive Digital Storytelling, ICIDS 2010, Proceedings (Vol. 6432 LNCS, pp. 275-278). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6432 LNCS). https://doi.org/10.1007/978-3-642-16638-9_41