Difference between revisions of "AdjutantBot"
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| + | This project focuses on the problem of avoiding troop loss by identifying good tactical groupings. Banding separated units together using UCT (Upper Confidence bounds applied to Trees) along with a learned reward model outperforms grouping heuristics at winning battles while preserving resources. Our Adjutant bot design won the best Newcomer honor at CIG 2012 | ||
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| + | ==Videos== | ||
| + | {{#ev:youtube|PfeB--1qaww}} | ||
| + | |||
| + | ==Scientific Publications== | ||
| + | * [http://ial.eecs.ucf.edu/Sukthankar-CIG2013.pdf Adjutant Bot: An Evaluation of Unit Micromanagement Tactics]. Nicholas Bowen*, Jonathan Todd*, and Gita Sukthankar. IEEE Conference on Computational Intelligence in Games (Competition). 2013. | ||
Latest revision as of 13:17, 23 July 2015
USA 
This project focuses on the problem of avoiding troop loss by identifying good tactical groupings. Banding separated units together using UCT (Upper Confidence bounds applied to Trees) along with a learned reward model outperforms grouping heuristics at winning battles while preserving resources. Our Adjutant bot design won the best Newcomer honor at CIG 2012
Videos
Scientific Publications
- Adjutant Bot: An Evaluation of Unit Micromanagement Tactics. Nicholas Bowen*, Jonathan Todd*, and Gita Sukthankar. IEEE Conference on Computational Intelligence in Games (Competition). 2013.
