Article thumbnail
Location of Repository

Proceedings of the Seventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Behavior Learning-Based Testing of Starcraft Competition Entries

By Michael Blackadar and Jörg Denzinger

Abstract

In this paper, we apply the idea of testing games by learning interactions with them that cause unwanted behavior of the game to test the competition entries for some of the scenarios of the 2010 StarCraft AI competition. By extending the previously published macro action concept to include macro action sequences for individual game units, by adjusting the concept to the realtime requirements of StarCraft, and by using macros involving specific abilities of game units, our testing system was able to find either weaknesses or system crashes for all of the competition entries of the chosen scenarios. Additionally, by requiring a minimal margin with respect to surviving units, we were able to clearly identify the weaknesses of the tested AIs

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.306.8549
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.aaai.org/ocs/index.... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.