@inproceedings{5f52303eb07f4f26909cb0d1a1f44113,
title = "High-level behavior control of an e-pet with reinforcement learning",
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.",
keywords = "E-pets, HTN planning, Markov decision process, Q-laerning, Reinforcement learning",
author = "Hsu, {Chih Wei} and Alan Liu",
year = "2010",
doi = "10.1109/ICSMC.2010.5642195",
language = "英语",
isbn = "9781424465880",
series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",
pages = "29--34",
booktitle = "2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010",
note = "2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010 ; Conference date: 10-10-2010 Through 13-10-2010",
}