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- W2011900082 abstract "One of attractive features of electronic-pets (e-pets) is interaction between the user and the e-pet. The interaction, however, is usually limited to using the predefined commands. In this paper, we present a way of involving the user in helping an e-pet learn high-level behaviors based on basic actions. The high-level behaviors are derived with planning, and the execution of the behaviors is then trained with reinforcement learning. In this research, we explain how we use a partially observable Markov decision process and the hierarchical task network planning for designing behaviors. A Q-learning method is then applied to the training of the e-pet for achieving the correct behavior. A prototype is presented to show its feasibility and effectiveness." @default.
- W2011900082 created "2016-06-24" @default.
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- W2011900082 date "2010-10-01" @default.
- W2011900082 modified "2023-09-24" @default.
- W2011900082 title "High-level behavior control of an e-pet with reinforcement learning" @default.
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- W2011900082 doi "https://doi.org/10.1109/icsmc.2010.5642195" @default.
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