@inproceedings{lu-etal-2020-learning,
title = "Learning and Reasoning for Robot Dialog and Navigation Tasks",
author = "Lu, Keting and
Zhang, Shiqi and
Stone, Peter and
Chen, Xiaoping",
editor = "Pietquin, Olivier and
Muresan, Smaranda and
Chen, Vivian and
Kennington, Casey and
Vandyke, David and
Dethlefs, Nina and
Inoue, Koji and
Ekstedt, Erik and
Ultes, Stefan",
booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = jul,
year = "2020",
address = "1st virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sigdial-1.14",
doi = "10.18653/v1/2020.sigdial-1.14",
pages = "107--117",
abstract = "Reinforcement learning and probabilistic reasoning algorithms aim at learning from interaction experiences and reasoning with probabilistic contextual knowledge respectively. In this research, we develop algorithms for robot task completions, while looking into the complementary strengths of reinforcement learning and probabilistic reasoning techniques. The robots learn from trial-and-error experiences to augment their declarative knowledge base, and the augmented knowledge can be used for speeding up the learning process in potentially different tasks. We have implemented and evaluated the developed algorithms using mobile robots conducting dialog and navigation tasks. From the results, we see that our robot{'}s performance can be improved by both reasoning with human knowledge and learning from task-completion experience. More interestingly, the robot was able to learn from navigation tasks to improve its dialog strategies.",
}
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%0 Conference Proceedings
%T Learning and Reasoning for Robot Dialog and Navigation Tasks
%A Lu, Keting
%A Zhang, Shiqi
%A Stone, Peter
%A Chen, Xiaoping
%Y Pietquin, Olivier
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Kennington, Casey
%Y Vandyke, David
%Y Dethlefs, Nina
%Y Inoue, Koji
%Y Ekstedt, Erik
%Y Ultes, Stefan
%S Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2020
%8 July
%I Association for Computational Linguistics
%C 1st virtual meeting
%F lu-etal-2020-learning
%X Reinforcement learning and probabilistic reasoning algorithms aim at learning from interaction experiences and reasoning with probabilistic contextual knowledge respectively. In this research, we develop algorithms for robot task completions, while looking into the complementary strengths of reinforcement learning and probabilistic reasoning techniques. The robots learn from trial-and-error experiences to augment their declarative knowledge base, and the augmented knowledge can be used for speeding up the learning process in potentially different tasks. We have implemented and evaluated the developed algorithms using mobile robots conducting dialog and navigation tasks. From the results, we see that our robot’s performance can be improved by both reasoning with human knowledge and learning from task-completion experience. More interestingly, the robot was able to learn from navigation tasks to improve its dialog strategies.
%R 10.18653/v1/2020.sigdial-1.14
%U https://aclanthology.org/2020.sigdial-1.14
%U https://doi.org/10.18653/v1/2020.sigdial-1.14
%P 107-117
Markdown (Informal)
[Learning and Reasoning for Robot Dialog and Navigation Tasks](https://aclanthology.org/2020.sigdial-1.14) (Lu et al., SIGDIAL 2020)
ACL
- Keting Lu, Shiqi Zhang, Peter Stone, and Xiaoping Chen. 2020. Learning and Reasoning for Robot Dialog and Navigation Tasks. In Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 107–117, 1st virtual meeting. Association for Computational Linguistics.