Kokolo Ikeda

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Kokolo Ikeda

Kokolo Ikeda,
a Japanese computer scientist, games researcher and associate professor at Japan Advanced Institute of Science and Technology. He holds a M.Sc. and Ph.D from Tokyo Institute of Technology in 2000 and 2003 respectively. His research interests include game informatics, Monte-Carlo tree search, evolutionary algorithm, machine learning, and agent-based simulation.

Ikeda Laboratory

Kokolo Ikeda is head of the Ikeda Laboratory at JAIST . Their goal is not only to make strong AI players for various games, but "realistic", "enjoyable" and/or "educational" agents. In addition, genetic algorithm, multi-objective optimization and multi-agent simulation are intensively studied.

Overview of GA and learning

Nomitan

As computer Go programmer, Kokolo Ikeda is co-author of the Go playing program Nomitan, an UCT based original program using an evaluation function optimized to game records of professional players. Nomitan won three times Bronze at Yokohama 2013, in 9x9, 13x13 and 19x19 Go .

Selected Publications

2000 ...

• Kokolo Ikeda, Shigenobu Kobayashi (2000). GA Based on the UV-Structure Hypothesis and Its Application to JSP. PPSN 2000
• Kokolo Ikeda, Shigenobu Kobayashi (2002). Deterministic Multi-step Crossover Fusion: A Handy Crossover Composition for GAs. PPSN 2002
• Kokolo Ikeda (2005). Exemplar-based direct policy search with evolutionary optimization. CEC 2005

2010 ...

• Kokolo Ikeda, Shigenobu Kobayashi, Hajime Kita (2010). Exemplar-Based Policy with Selectable Strategies and its Optimization Using GA. Transactions of the Japanese Society for Artificial Intelligence, Vol. 25, No. 2
• Junichi Hashimoto, Akihiro Kishimoto, Kazuki Yoshizoe, Kokolo Ikeda (2011). Accelerated UCT and Its Application to Two-Player Games. Advances in Computer Games 13
• Kokolo Ikeda, Simon Viennot, et al. (2012). Adaptation of game AIs using Genetic Algorithm: Keeping variety and suitable strength. ISIS 2012
• Kokolo Ikeda, Daisuke Tomizawa, Simon Viennot, Yuu Tanaka (2012). Playing PuyoPuyo: Two search algorithms for constructing chain and tactical heuristics. CIG 2012
• Kokolo Ikeda, Simon Viennot (2013). Production of various strategies and position control for Monte-Carlo Go - Entertaining human players. CIG 2013
• Simon Viennot, Kokolo Ikeda (2013). Efficiency of Static Knowledge Bias in Monte-Carlo Tree Search. CG 2013
• Kristian Spoerer, Toshihisa Okaneya, Kokolo Ikeda, Hiroyuki Iida (2013). Further Investigations of 3-Member Simple Majority Voting for Chess. CG 2013

2015 ...

• Kokolo Ikeda, Takanari Shishido, Simon Viennot (2015). Machine-Learning of Shape Names for the Game of Go. Advances in Computer Games 14
• Kokolo Ikeda, Simon Viennot, Naoyuki Sato (2016). Detection and labeling of bad moves for coaching go. CIG 2016
• Naoyuki Sato, Kokolo Ikeda (2016). Three types of forward pruning techniques to apply the alpha beta algorithm to turn-based strategy games. CIG 2016
• Taishi Oikawa, Chu-Hsuan Hsueh, Kokolo Ikeda (2019). Improving Human Players’ T-Spin Skills in Tetris with Procedural Problem Generation. Advances in Computer Games 16
• Tomihiro Kimura, Kokolo Ikeda (2019). Designing Policy Network with Deep Learning in Turn-Based Strategy Games. Advances in Computer Games 16

2020 ...

• Keita Fujihira, Chu-Hsuan Hsueh, Kokolo Ikeda (2021). Procedural Maze Generation with Considering Difficulty from Human Players’ Perspectives. Advances in Computer Games 17

External Links

• JAIST School of Information Science - Kokolo Ikeda - Associate Professor
• Entertainment Informatics / Ikeda Laboratory | JAIST School of Information Science
• Kokolo Ikeda's ICGA Tournaments

References

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