TY - GEN
T1 - Portfolio Online Evolution in StarCraft
AU - Wang, Che
AU - Chen, Pan
AU - Li, Yuanda
AU - Holmgård, Christoffer
AU - Togelius, Julian
N1 - Publisher Copyright:
Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2016/10/8
Y1 - 2016/10/8
N2 - Portfolio Online Evolution is a novel method for playing real-time strategy games through evolutionary search in the space of assignments of scripts to individual game units. This method builds on and recombines two recently devised methods for playing multi-action games: (1) Portfolio Greedy Search, which searches in the space of heuristics assigned to units rather than in the space of actions, and (2) Online Evolution, which uses evolution rather than tree search to effectively play games where multiple actions per turn lead to enormous branching factors. The combination of both ideas lead to the use of evolution to search the space of which script/heuristic is assigned to which unit. In this paper, we introduce the ideas of Portfolio Online Evolution and apply it to StarCraft micro, or individual battles. It is shown to outperform all other tested methods in battles of moderate to large size.
AB - Portfolio Online Evolution is a novel method for playing real-time strategy games through evolutionary search in the space of assignments of scripts to individual game units. This method builds on and recombines two recently devised methods for playing multi-action games: (1) Portfolio Greedy Search, which searches in the space of heuristics assigned to units rather than in the space of actions, and (2) Online Evolution, which uses evolution rather than tree search to effectively play games where multiple actions per turn lead to enormous branching factors. The combination of both ideas lead to the use of evolution to search the space of which script/heuristic is assigned to which unit. In this paper, we introduce the ideas of Portfolio Online Evolution and apply it to StarCraft micro, or individual battles. It is shown to outperform all other tested methods in battles of moderate to large size.
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M3 - Conference contribution
AN - SCOPUS:85170573696
T3 - Proceedings - AAAI Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE
SP - 114
EP - 120
BT - Proceedings of the 12th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016
A2 - Sturtevant, Nathan
A2 - Magerko, Brian
PB - Association for the Advancement of Artificial Intelligence
T2 - 12th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016
Y2 - 8 October 2016 through 12 October 2016
ER -