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| Content Provider | Springer Nature Link |
|---|---|
| Author | Köstler, Harald Gmeiner, Björn |
| Copyright Year | 2013 |
| Abstract | This article presents a modified version of the multi-objective genetic algorithm NSGA II in order to find optimal opening strategies in the real-time strategy game StarCraft II. Based on an event-driven simulator capable of performing an accurate estimate of in-game build-times the quality of different build lists can be judged. These build lists are used as chromosomes within the genetic algorithm. Procedural constraints e.g. given by the Tech-Tree or other game mechanisms, are implicitly encoded into them. Typical goals are to find the build list producing most units of one or more certain types up to a certain time (Rush) or to produce one unit as early as possible (Tech-Push). Here, the number of entries in a build list varies and the objective values have in contrast to the search space a very small diversity. We introduce our game simulator including its graphical user interface, the modifications necessary to fit the genetic algorithm to our problem, test our algorithm on different Tech-Pushes and Rushes for all three races, and validate it with empirical data of expert StarCraft II players. |
| Starting Page | 221 |
| Ending Page | 233 |
| Page Count | 13 |
| File Format | |
| ISSN | 09331875 |
| Journal | KI - Künstliche Intelligenz |
| Volume Number | 27 |
| Issue Number | 3 |
| e-ISSN | 16101987 |
| Language | English |
| Publisher | Springer Berlin Heidelberg |
| Publisher Date | 2013-06-29 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Genetic algorithm Multi-objective optimization NSGA II Starcraft II Artificial Intelligence (incl. Robotics) Software Engineering/Programming and Operating Systems |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence |
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