CILAB Introduction

Research Fields

Game AI

There are three interesting research topics in game AI field: Game AI Player, Game Contents Generation, and Game Player Modeling. If you like games and want to apply modern artificial intelligence techniques on them, please contact us.

Deep Reinforcement Learning

Traditionally, machine learning can be categorized into three types: Supervised learning, unsupervised learning and reinforcement learning. Recently, the hybrid of reinforcement learning and deep learning has made big contribution into the field of automatic decision making. If you want to join to the world of modern deep reinforcement learning, please contact us.

Applications of Deep Learning for Healthcare, Culture,
Technology, and Autonomous Car

We're interested in using modern deep learning techniques into the real-world applications. For example, we're involved in studying healthcare, culture technology and autonomous car problems.

Game Data Mining

Games naturally produce tons of data from game players and the big data help game designers understand their customers. Especially, game companies attempt to predict game players' future behaviors such as churn and purchase.

Current Projects


Research to solve complex video games.Solve complex problems by applying recognition models, in-depth structures, and multimodal to virtual environments based on Deep Reinforcement Learning.


Predict gamers' behavior based on multiple game logs.Preprocess and analyze the log data of the game company.


A project to conduct self-driving research on night/hazardous areas.Apply to the actual vehicle using models learned from reinforcement training in the simulator.

Interested in our research?

We're hiring motivated interns and graduate students!

Find us at the office

123 Cheomdangwagi-ro, Buk-gu, Gwangju, 61005, Korea

Bdg. Dasan

Give us a ring

Prof. Kyung-Joong Kim