19 citations · 44 across the 12 of their papers we have counts for
4 papers · 1 filter
Refining Manually-Designed Symbol Grounding and High-Level Planning by Policy Gradients
Takuya Hiraoka, Takashi Onishi, Takahisa Imagawa +1
Hierarchical planners that produce interpretable and appropriate plans are desired, especially in its application to supporting human decision making. In the typical development of…
Monte Carlo Tree Search with Scalable Simulation Periods for Continuously Running Tasks
Seydou Ba, Takuya Hiraoka, Takashi Onishi +2
Monte Carlo Tree Search (MCTS) is particularly adapted to domains where the potential actions can be represented as a tree of sequential decisions. For an effective action selectio…
Hierarchical Reinforcement Learning with Abductive Planning
Kazeto Yamamoto, Takashi Onishi, Yoshimasa Tsuruoka
One of the key challenges in applying reinforcement learning to real-life problems is that the amount of train-and-error required to learn a good policy increases drastically as th…
Translating MFM into FOL: towards plant operation planning
Shota Motoura, Kazeto Yamamoto, Shumpei Kubosawa +1
This paper proposes a method to translate multilevel flow modeling (MFM) into a first-order language (FOL), which enables the utilisation of logical techniques, such as inference e…