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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Siming Liu Louis, S.J. Nicolescu, M. |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Comput. Sci. & Eng., Univ. of Nevada, Reno, Reno, NV, USA (Siming Liu; Louis, S.J.; Nicolescu, M.) |
| Abstract | We compare genetic algorithms against hill-climbers for generating competitive unit micro-management for winning real-time strategy game skirmishes. Good group positioning and movement, which are part of unit micro-management can help win skirmishes against equal numbers and types of opponent units or even when outnumbered. In this paper, we use influence maps to generate group positioning and potential fields to guide unit movement. We tested the behaviors obtained from genetic algorithm and two types of hill-climbing search against the default Starcraft AI using the brood war API. Preliminary results show that while our hill-climbers quickly find influence maps and potential fields that generate quality positioning and movement in our simulations, they only find quality solutions fifty to seventy percent of the time. On the other hand, genetic algorithms evolve high quality solutions a hundred percent of the time, but take significantly longer. |
| Starting Page | 1371 |
| Ending Page | 1378 |
| File Size | 973922 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479904532 |
| e-ISBN | 9781479904549 |
| e-ISBN | 9781479904525 |
| DOI | 10.1109/CEC.2013.6557724 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-20 |
| Publisher Place | Mexico |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Games Genetic algorithms Artificial intelligence Force Biological cells Sociology Statistics |
| Content Type | Text |
| Resource Type | Article |
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