리니지 PC (리마스터)환경에서 강화학습 AI가 직접 학습하기 어려운 장거리 이동을 처리할 수 있는 플레이어 매니저 AI 구현
리니지 PC 환경에서 python 코드부분을 최적화
StarCraft II 환경에서 다양한 플레이 스타일 학습 실험 2019-01 ~ 2020-02
PBT와 QD (MAP-Elites)를 결합
StarCraft II AI 연구 2018-10-2018-12
공개된 StarCraft II 플랫폼을 이용해서 Game AI를 위한 강화학습 등 기계학습 기술을 연구/개발
플랫폼의 특성을 파악하고 관련된 최신기술을 조사 및 실험
NC Fellowship 경진대회 플랫폼/예제 개발 2018-01 ~ 2018-12
학부생을 대상으로한 Game AI 개발 경진대회에서 사용할 플랫폼과 예제 개발
미니 체스의 일종인 마이크로 체스를 기반으로 플랫폼 개발하고, AlphaGo와 유사한 MCTS를 사용하는 self-play learning 예제 구현
Maestro 2015-07 ~ 2016-06
모바일 RTS게임 개발프로젝트
화면이 작고 조작이 어려운 모바일 환경에서 RTS 게임을 수월하게 플레이 할 수 있도록 플레이어를 보조해주는 부관 AI를 만드는 업무 수행
2021
2020
2019
Publications
2022
M.-J. Kim, H.-S. Park, and C.-W. Ahn, “Nondominated Policy-Guided Learning in Multi-Objective Reinforcement Learning.” Electronics, 11.7, MDPI, 2022.
2017
H.-S. Park, and K.-J. Kim, “Automatic Learning Rate Adjustment for Deep Reinforcement Learning using Evolutionary Algorithm on Multiple Machines,” VGML (Video Games and Machine Learning) workshop @ICML2017. (accepted)
2016
H.-S. Park, and K.-J. Kim, “Deep Q-Learning using Redundant Outputs in Visual Doom”, IEEE Conference on Computational Intelligence and Games, 2016.
H.-S. Park and K.-J. Kim, “Active Player Modeling in the Iterated Prisoner's Dilemma,” Computational Intelligence and Neuroscience, 2016 Link.
H.-C. Cho, H.-S. Park, C.-Y. Kim and K.-J. Kim, “Investigation of the Effect of “Fog of War” in the Prediction of StarCraft Strategy Using Machine Learning.” Computers in Entertainment (CIE), ACM Computers in Entertainment (CIE), vol. 14.1, no.2, 2016.
2015
H.-S. Park, and K.-J. Kim, “MCTS with Influence Map for General Video Game Playing,” IEEE Conference on Computational Intelligence and Games, 2015.(pp.534-535)
H.-S. Park, H.-T. Kim, and K.-J. Kim, “GreedyUCB1 based Monte-Carlo Tree Search for General Video Game Playing Artificial Intelligence,” KIISE Transactions on Computing Practices, vol. 21, no. 8, pp. 572-577, Aug 2015.
M. Swiechowski, H.-S. Park, J. Mańdziuk and K.-J. Kim,“Recent Advances in General Game Playing,” The Scientific World Journal, 2015.
2014
S. S. Farooq, H.-S. Park, and K.-J. Kim, “Inference of Opponent's Uncertain States in Ghost Game using Machine Learning,” The 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, 2014 (pp. 335-346)
H.-S. Park, and K.-J. Kim, “Social Network Analysis of High-Level Players in Multiplayer Online Battle Arena Game,” The First Exploration on Games and Gamers Workshop @ SocInfo 2014.
H.-S. Park and K.-J. Kim, “Learning to Play Fighting Game using Massive Play Data,” IEEE Conference on Computational Intelligence and Games, 2014.
2013
H.-S. Park, and K.-J. Kim, “Opponents modeling with incremental active learning: A case study of iterative prisoner's dilemma,” IEEE Conference on Computational Intelligence in Games, 2013.
H.-S. Park and K.-J. Kim, “Active Data Collection and Opponent Player Modeling using Estimation-Exploration Algorithm and Evolutionary Neural Network,” KIISE Fall Conference, 2013.
H.-S. Park, D.-M. Yoon, H.-C. Cho, and K.-J. Kim, “Applications of machine learning for games: Case studies,” IEEK Summer Conference, 2013.
H.-S. Park and K.-J. Kim, “Opponent's player's decision modeling in iterated prisoner's dilemma using reverse engineering technique,” Korea Congress on Computer, 2013.
H.-S. Park, and K.-J. Kim, “Recent Research Trends in Game Artificial Intelligence,” Communications of the Korean Institute of Information Scientists and Engineers, July 2013.
H.-S. Park, and K.-J. Kim, “The Automated Fault-Recovery for Four-Legged Robots using Parallel Genetic Algorithm,” Procedia Computer Science, pp. 158-166, 2013.
J.-E. Lee, H.-S. Park, K.-J. Kim and J.-C. No, “Learning to Predict the Need of Summarization on News Articles,” Procedia Computer Science, pp. 274-279, 2013.
2012
H.-S. Park, H.-C. Cho, K.-Y. Lee, and K.-J. Kim, “Prediction of early stage opponent strategy for StarCraft AI using scouting and machine learning,” Workshop at SIGGRAPH ASIA (Computer Gaming Track), pp. 7-12, 2012.
H.-S. Park and K.-J. Kim, “Automatic python programming using stack-based genetic programming,” Genetic and Evolutionary Computation Conference (GECCO), pp. 641-642, 2012.
H.-S. Park and K.-J. Kim, “Evolving a neural controller for fault-tolerant four-legged robots using parallel genetic algorithm,” International Conference on Advanced Information Technology and Sensor Application, p. 129, 2012.
H.-S. Park, and K.-J. Kim, “Python bytecode evolution using stack-based genetic programming for regression problems,” Symposium on Brain and Artificial Intelligence, 2012 (Abstract).
H.-S. Park, A.-R. Park and K.-J. Kim, “Fault-tolerant analog circuit design using average and worst case analysis evolutionary strategy,” Korea Congress on Computer 2012.
2011
H.-S. Park and K.-J. Kim, “Automated synthesis of physically implementable analog circuits using evolutionary strategy and practical constraints on component values,” Journal of KIISE: Software and Applications, vol. 38, no. 5, pp. 248-256, 2011.
H.-S. Park, and K.-J. Kim, “Genetic Programming using Bytecode-Level Evolution for Python Programming Language,” KIISE Fall Conference, 2011.
H.-S. Park, and K.-J. Kim, “Research on scalability of evolutionary analog circuits: Band-pass filter case study,” HCI 2011 (accepted).
2010
H.-S. Park, and K.-J. Kim, “Systematic evaluation of island based real-valued genetic algorithm with graphics processing unit,” Korea Congress on Computer, vol. 37, no. 1(C), pp. 328-333, 2010.