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ORCIDKokolo Ikeda
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2020 – today
- 2025
[j6]Chu-Hsuan Hsueh
, Takefumi Ishii, Tsuyoshi Hashimoto, Kokolo Ikeda:
Proposal and generation of endgame puzzles for an imperfect information game Geister. Entertain. Comput. 52: 100736 (2025)- 2024
- [j5]Chu-Hsuan Hsueh, Kokolo Ikeda:
Improvement of move naturalness for playing good-quality games with middle-level players. Appl. Intell. 54(2): 1637-1655 (2024) - [c50]Tatsuyoshi Ogawa, Kei Nakagawa, Kokolo Ikeda:
Optimal Execution Strategy Using Deep Q-Network with Heuristics Policy. IIAI-AAI 2024: 456-461 - 2023
- [j4]Chu-Hsuan Hsueh, Kokolo Ikeda, I-Chen Wu, Jr-Chang Chen, Tsan-sheng Hsu:
Analyses of Tabular AlphaZero on Strongly-Solved Stochastic Games. IEEE Access 11: 18157-18182 (2023) - [c49]Chu-Hsuan Hsueh, Kokolo Ikeda:
Can We Infer Move Sequences in Go from Stone Arrangements? ACG 2023: 76-87 - [c48]Tatsuyoshi Ogawa, Chu-Hsuan Hsueh, Kokolo Ikeda:
Improving the Human-Likeness of Game AI's Moves by Combining Multiple Prediction Models. ICAART (3) 2023: 931-939 - [c47]SangGyu Nam, Chu-Hsuan Hsueh, Kokolo Ikeda:
Procedural Content Generation of Super Mario Levels Considering Natural Connection. JCSSE 2023: 291-296 - [c46]Kyota Kuboki, Chu-Hsuan Hsueh, Kokolo Ikeda:
Factor Analyses on Positive and Negative Evaluations of Games against Go Programs. TAAI (1) 2023: 343-357 - 2022
- [j3]Sang-Gyu Nam, Chu-Hsuan Hsueh, Kokolo Ikeda:
Generation of Game Stages With Quality and Diversity by Reinforcement Learning in Turn-Based RPG. IEEE Trans. Games 14(3): 488-501 (2022) - [c45]Chu-Hsuan Hsueh, Kokolo Ikeda:
Playing Good-Quality Games with Weak Players by Combining Programs with Different Roles. CoG 2022: 612-615 - [c44]Wanxiang Li, Houkuan He, Chu-Hsuan Hsueh, Kokolo Ikeda:
Graph Convolutional Networks for Turn-Based Strategy Games. ICAART (2) 2022: 552-561 - 2021
- [j2]Kenji Uehara, Kunihiko Hiraishi, Kokolo Ikeda:
An Efficient Aircraft Boarding Strategy Considering Implementation. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 104-A(8): 1051-1058 (2021) - [j1]Yuan Shi, Tianwen Fan, Wanxiang Li, Chu-Hsuan Hsueh, Kokolo Ikeda:
Position Control and Production of Various Strategies for Game of Go Using Deep Learning Methods. J. Inf. Sci. Eng. 37(3): 553-573 (2021) - [c43]Keita Fujihira, Chu-Hsuan Hsueh, Kokolo Ikeda:
Procedural Maze Generation Considering Difficulty from Human Players' Perspectives. ACG 2021: 165-175 - 2020
- [c42]Wanxiang Li, Chu-Hsuan Hsueh, Kokolo Ikeda:
Imitating Agents in A Complex Environment by Generative Adversarial Imitation Learning. CoG 2020: 702-705 - [c41]Tomihiro Kimura, Kokolo Ikeda:
High-performance Algorithms using Deep Learning in Turn-based Strategy Games. ICAART (2) 2020: 555-562 - [c40]Chu-Hsuan Hsueh, Kokolo Ikeda, Sang-Gyu Nam, I-Chen Wu:
Analyses of Tabular AlphaZero on NoGo. TAAI 2020: 254-259
2010 – 2019
- 2019
- [c39]Taishi Oikawa, Chu-Hsuan Hsueh, Kokolo Ikeda:
Improving Human Players' T-Spin Skills in Tetris with Procedural Problem Generation. ACG 2019: 41-52 - [c38]Tomihiro Kimura, Kokolo Ikeda:
Designing Policy Network with Deep Learning in Turn-Based Strategy Games. ACG 2019: 143-154 - [c37]SangGyu Nam, Kokolo Ikeda:
Generation of Diverse Stages in Turn-Based Role-Playing Game using Reinforcement Learning. CoG 2019: 1-8 - [c36]Yubin Liang, Wanxiang Li, Kokolo Ikeda:
Procedural Content Generation of Rhythm Games Using Deep Learning Methods. ICEC-JCSG 2019: 134-145 - [c35]Tianwen Fan, Yuan Shi, Wanxiang Li, Kokolo Ikeda:
Position Control and Production of Various Strategies for Deep Learning Go Programs. TAAI 2019: 1-6 - [r1]Kokolo Ikeda:
Puyo Puyo. Encyclopedia of Computer Graphics and Games 2019 - 2017
- [c34]Luong Huu Phuc, Kanazawa Naoto, Kokolo Ikeda:
Learning human-like behaviors using neuroevolution with statistical penalties. CIG 2017: 207-214 - [c33]Nguyen Duc Tang Tri, Vu Quang, Kokolo Ikeda:
Optimized Non-visual Information for Deep Neural Network in Fighting Game. ICAART (2) 2017: 676-680 - [c32]Sila Temsiririrkkul, Naoyuki Sato, Kenta Nakagawa, Kokolo Ikeda:
Survey of How Human Players Divert In-game Actions for Other Purposes: Towards Human-Like Computer Players. ICEC 2017: 243-256 - 2016
- [c31]Kokolo Ikeda, Simon Viennot, Naoyuki Sato:
Detection and labeling of bad moves for coaching go. CIG 2016: 1-8 - [c30]Naoyuki Sato, Kokolo Ikeda:
Three types of forward pruning techniques to apply the alpha beta algorithm to turn-based strategy games. CIG 2016: 1-8 - 2015
- [c29]Kokolo Ikeda, Takanari Shishido, Simon Viennot:
Machine-Learning of Shape Names for the Game of Go. ACG 2015: 247-259 - [c28]Naoyuki Sato, Sila Temsiririrkkul, Shogo Sone, Kokolo Ikeda:
Adaptive Fighting Game Computer Player by Switching Multiple Rule-Based Controllers. ACIT-CSI 2015: 52-59 - [c27]Naoyuki Sato, Kokolo Ikeda, Takayuki Wada:
Estimation of player's preference for cooperative RPGs using multi-strategy Monte-Carlo method. CIG 2015: 51-59 - [c26]Tsubasa Fujiki, Kokolo Ikeda, Simon Viennot:
A platform for turn-based strategy games, with a comparison of Monte-Carlo algorithms. CIG 2015: 407-414 - 2014
- [c25]Sila Temsiririrkkul, Hisamitsu Nomura, Kokolo Ikeda:
Biased random sequence generation for making common player believe it unbiased. GEM 2014: 1-8 - [c24]Huy Nguyen, Simon Viennot, Kokolo Ikeda:
Fast Optimization of the Pattern Shapes in Board Games with Simulated Annealing. KSE 2014: 325-337 - 2013
- [c23]Kokolo Ikeda, Simon Viennot:
Efficiency of Static Knowledge Bias in Monte-Carlo Tree Search. Computers and Games 2013: 26-38 - [c22]Kristian Toby Spoerer, Toshihisa Okaneya, Kokolo Ikeda, Hiroyuki Iida:
Further Investigations of 3-Member Simple Majority Voting for Chess. Computers and Games 2013: 199-207 - [c21]Kokolo Ikeda, Simon Viennot:
Production of various strategies and position control for Monte-Carlo Go - Entertaining human players. CIG 2013: 1-8 - 2012
- [c20]Kokolo Ikeda, Daisuke Tomizawa, Simon Viennot, Yuu Tanaka:
Playing PuyoPuyo: Two search algorithms for constructing chain and tactical heuristics. CIG 2012: 71-78 - [c19]Kokolo Ikeda, Yu Tanaka, Simon Viennot, Nguyen Huy Quoc, Yohei Ueda:
Adaptation of game AIs using Genetic Algorithm: Keeping variety and suitable strength. SCIS&ISIS 2012: 945-951 - [c18]Huy Nguyen, Kokolo Ikeda, Bac Le:
Extracting Important Patterns for Building State-Action Evaluation Function in Othello. TAAI 2012: 278-283 - 2011
- [c17]Junichi Hashimoto, Akihiro Kishimoto, Kazuki Yoshizoe, Kokolo Ikeda:
Accelerated UCT and Its Application to Two-Player Games. ACG 2011: 1-12 - 2010
- [c16]Hiroki Saito, Tetsuya Abe, Kokolo Ikeda, Kenji Ohira, Mikihiko Mori, Tetsutaro Uehara, Hajime Kita:
An Agent-Based Approach to Evaluation of Workflow Support Systems. C5 2010: 51-55
2000 – 2009
- 2009
- [c15]Masaki Saga, Kokolo Ikeda, Mikihiko Mori, Tetsutaro Uehara, Hajime Kita, Yohei Naya, Naomi Nagata, Hiroto Ueda, Akio Okumura, Terufumi Ohno:
Development of a Multiple User Quiz System on a Shared Display. C5 2009: 103-110 - [c14]Hirotaka Kaji, Kokolo Ikeda, Hajime Kita:
Uncertainty of constraint function in Evolutionary Multi-objective Optimization. IEEE Congress on Evolutionary Computation 2009: 1621-1628 - [c13]Hirotaka Kaji, Kokolo Ikeda, Hajime Kita:
Avoidance of constraint violation for experiment-based evolutionary multi-objective optimization. IEEE Congress on Evolutionary Computation 2009: 2756-2763 - 2008
- [c12]Hirotaka Kaji, Kokolo Ikeda, Hajime Kita:
Acceleration of parametric Multi-objective Optimization by an initialization technique for Multi-Objective Evolutionary Algorithms. IEEE Congress on Evolutionary Computation 2008: 2291-2297 - [c11]Mikihiko Mori, Kokolo Ikeda, Gaku Hagiwara, Masaki Saga, Tetsutaro Uehara, Hajime Kita:
Socialized Computers and Collaborative Learning. JSAI 2008: 48-61 - 2007
- [c10]Gaku Hagiwara, Kokolo Ikeda, Mikihiko Mori, Tetsutaro Uehara, Hajime Kita:
On Socialization of Personal Computing. C5 2007: 71-75 - [c9]Kokolo Ikeda, Hajime Kita:
State evaluation strategy for exemplar-based policy optimization of dynamic decision problems. IEEE Congress on Evolutionary Computation 2007: 3685-3691 - [c8]Kokolo Ikeda, Hiromichi Suzuki, Sandor Markon, Hajime Kita:
Designing Traffic-Sensitive Controllers for Multi-Car Elevators Through Evolutionary Multi-objective Optimization. EMO 2007: 673-686 - 2006
- [c7]Chikao Tsuchiya, Kokolo Ikeda, Jun Sakuma, Isao Ono, Shigenobu Kobayashi:
Instance-Based Policy Search using Binomial Distribution Crossover and Iterated Refreshment. IEEE Congress on Evolutionary Computation 2006: 378-385 - [c6]Ken Harada, Kokolo Ikeda, Shigenobu Kobayashi:
Hybridization of genetic algorithm and local search in multiobjective function optimization: recommendation of GA then LS. GECCO 2006: 667-674 - 2005
- [c5]Kokolo Ikeda:
Exemplar-based direct policy search with evolutionary optimization. Congress on Evolutionary Computation 2005: 2357-2364 - 2003
- [c4]Kokolo Ikeda, Akihiro Nagaiwa, Kei Aoki, Shigenobu Kobayashi:
Independent constraint satisfaction and its application to sewerage system control. IEEE Congress on Evolutionary Computation 2003: 566-573 - 2002
- [c3]Kokolo Ikeda, Shigenobu Kobayashi:
Deterministic Multi-step Crossover Fusion: A Handy Crossover Composition for GAs. PPSN 2002: 162-171 - 2001
- [c2]Kokolo Ikeda, Hajime Kita, Shigenobu Kobayashi:
Failure of Pareto-based MOEAs: does non-dominated really mean near to optimal? CEC 2001: 957-962 - 2000
- [c1]Kokolo Ikeda, Shigenobu Kobayashi:
GA Based on the UV-Structure Hypothesis and Its Application to JSP. PPSN 2000: 273-282
Coauthor Index
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