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Learning and Reasoning for Robot Dialog and Navigation Tasks.
Keting Lu, Shiqi
Zhang, Peter Stone, and Xiaoping
Chen.
In Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp.
107–117, Association for Computational Linguistics, 1st virtual meeting, July 2020.
Official version from ACL
Digital Library, including a link to the conference presentation
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.
@InProceedings{SIGDIAL20,
author = {Keting Lu and Shiqi Zhang and Peter Stone and Xiaoping Chen},
title = {Learning and Reasoning for Robot Dialog and Navigation Tasks},
booktitle = {Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue},
month = {July},
year = {2020},
address = {1st virtual meeting},
publisher = {Association for Computational Linguistics},
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.},
wwwnote={Official version from <a href="https://www.aclweb.org/anthology/2020.sigdial-1.14/">ACL Digital Library</a>, including a link to the conference presentation},
}
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