@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00175357,
 author = {鎌田, 徹朗 and 橋本, 剛 and Tetsuro, Kamada and Tsuyoshi, Hashimoto},
 book = {ゲームプログラミングワークショップ2016論文集},
 month = {Oct},
 note = {StarCraftはRTS（Real-Time Strategy）ゲームの中でも特に人気のシリーズであり，多くのプロが存在している．現在までに，プロより強いAI開発を目標として様々な研究が行われているが，AIはプロに対して0勝15敗と惨敗しており，プロのレベルには遠い．本研究ではより効率的なAIの強化を考え，StarCraftの作戦に注目し，深層学習を用いた対戦相手の作戦予測手法を提案する．, StarCraft is a particularly popular series of RTS (Real Time Strategy) games with which many professional players play. Various studies aimed at development of StarCraft AI that is stronger than professional players have performed to date, but the AI played with professional players and that result is zero win fifteen loses among fifteen games form AI. It shows that AI is far from professional level. Considering effectively reinforcement of StarCraft AI, this reserch introduce a prediction of opponent stragegy using deep lerning.},
 pages = {167--171},
 publisher = {情報処理学会},
 title = {深層学習を用いたStarCraftの敵作戦予測},
 volume = {2016},
 year = {2016}
}