Collaborated with:Hyun-Soo Park S.Cho Ho-Chul Cho Hyun-Tae Kim In-Seok Oh Du-Mim Yoon Byungho Yoo Man-Je Kim Byeong-Jun Min Seonghun Yoon In-Chang Baek Ho-Taek Joo H.Choi Sehar Shahzad Farooq Jong-Woong Baek JiHoon Jeon DuMim Yoon Seong-Il Yang Cheong-mok Bae Eun Kwang Kim Jongchan Lee Joong Chae Na Joo-Seon Lee Hyun-Su Seon Jeong-Hyeon Kim
Talks about:game (12) learn (8) use (6) play (5) base (5) visual (4) craft (4) star (4) data (4) strategi (3)
Person: Kyung-Joong Kim
DBLP: Kim:Kyung=Joong
Contributed to:
200720082013201420152016201720182019Wrote 22 papers:
- CIG-2007-KimCC #evolution #hybrid #learning
- Hybrid of Evolution and Reinforcement Learning for Othello Players (KJK, HC, SBC), pp. 203–209.
- CIG-2008-KimC #case study #game studies
- Ensemble approaches in evolutionary game strategies: A case study in Othello (KJK, SBC), pp. 212–219.
- CIG-2013-ChoK #comparison #data mining #mining
- Comparison of human and AI bots in StarCraft with replay data mining (HCC, KJK), pp. 1–2.
- CIG-2013-ChoKC #adaptation #order #predict
- Replay-based strategy prediction and build order adaptation for StarCraft AI bots (HCC, KJK, SBC), pp. 1–7.
- CIG-2013-ParkK #case study #incremental #learning #modelling
- Opponent modeling with incremental active learning: A case study of Iterative Prisoner's Dilemma (HSP, KJK), pp. 1–2.
- CIG-2014-KimK #game studies #learning #realtime #recommendation
- Learning to recommend game contents for real-time strategy gamers (HTK, KJK), pp. 1–8.
- CIG-2014-KimYK #geometry #graph #monte carlo #representation #using
- Solving Geometry Friends using Monte-Carlo Tree Search with directed graph representation (HTK, DMY, KJK), pp. 1–2.
- CIG-2014-OhCK #game studies #learning
- Imitation learning for combat system in RTS games with application to starcraft (ISO, HCC, KJK), pp. 1–2.
- CIG-2014-ParkK #game studies #learning #using
- Learning to play fighting game using massive play data (HSP, KJK), pp. 1–2.
- CIG-2015-BaeKLKN #generative #scalability
- Generation of an arbitrary shaped large maze by assembling mazes (CmB, EKK, JL, KJK, JCN), pp. 538–539.
- CIG-2015-FarooqBK #behaviour #game studies #mobile
- Interpreting behaviors of mobile game players from in-game data and context logs (SSF, JWB, KJK), pp. 548–549.
- CIG-2015-OhK #reliability #testing
- Testing reliability of replay-based imitation for StarCraft (ISO, KJK), pp. 536–537.
- CIG-2015-ParkK #game studies #video
- MCTS with influence map for general video game playing (HSP, KJK), pp. 534–535.
- CIG-2015-YoonLSKK #distributed #optimisation
- Optimization of Angry Birds AI controllers with distributed computing (DMY, JSL, HSS, JHK, KJK), pp. 544–545.
- CIG-2016-ParkK #using #visual notation
- Deep Q-learning using redundant outputs in visual doom (HSP, KJK), pp. 1–2.
- CIG-2016-YooK #algorithm #game studies #using #video
- Changing video game graphic styles using neural algorithms (BY, KJK), pp. 1–2.
- CIG-2017-JeonYYK #game studies #mobile #performance #predict
- Extracting gamers' cognitive psychological features and improving performance of churn prediction from mobile games (JJ, DY, SIY, KJK), pp. 150–153.
- CIG-2017-KimK #game studies #modelling
- Opponent modeling based on action table for MCTS-based fighting game AI (MJK, KJK), pp. 178–180.
- CIG-2017-MinK #game studies #learning #using #visual notation
- Learning to play visual doom using model-free episodic control (BJM, KJK), pp. 223–225.
- CIG-2017-YoonK #game studies #network #visual notation
- Deep Q networks for visual fighting game AI (SY, KJK), pp. 306–308.
- CIG-2018-BaekK #interface
- Web-Based Interface for Data Labeling in StarCraft (ICB, KJK), pp. 1–2.
- CoG-2019-JooK #game studies #how #learning #question #using #visualisation
- Visualization of Deep Reinforcement Learning using Grad-CAM: How AI Plays Atari Games? (HTJ, KJK), pp. 1–2.