A reinforcement learning scheme for a multi-agent card game. - Hajime Fujita 001; Yoichiro Matsuno; Shin Ishii (SMC, 2003)

Model-based reinforcement learning for a multi-player card game with partial observability. - Hajime Fujita 001; Shin Ishii (IAT, 2005)

A Reinforcement Learning Scheme for a Partially-Observable Multi-Agent Game. - Shin Ishii; Hajime Fujita 001; Masaoki Mitsutake; Tatsuya Yamazaki; Jun Matsuda; Yoichiro Matsuno (Mach. Learn., 2005)

Feature Extraction for Decision-Theoretic Planning in Partially Observable Environments. - Hajime Fujita 001; Yutaka Nakamura; Shin Ishii (ICANN (1), 2006)

Model-Based Reinforcement Learning for Partially Observable Games with Sampling-Based State Estimation. - Hajime Fujita 001; Shin Ishii (Neural Comput., 2007)