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.- Anthology ID:
- 2020.sigdial-1.14
- Original:
- 2020.sigdial-1.14v1
- Version 2:
- 2020.sigdial-1.14v2
- Volume:
- Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Month:
- July
- Year:
- 2020
- Address:
- 1st virtual meeting
- Editors:
- Olivier Pietquin, Smaranda Muresan, Vivian Chen, Casey Kennington, David Vandyke, Nina Dethlefs, Koji Inoue, Erik Ekstedt, Stefan Ultes
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 107–117
- Language:
- URL:
- https://aclanthology.org/2020.sigdial-1.14
- DOI:
- 10.18653/v1/2020.sigdial-1.14
- Cite (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.
- Cite (Informal):
- Learning and Reasoning for Robot Dialog and Navigation Tasks (Lu et al., SIGDIAL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2020.sigdial-1.14.pdf