Towards Problem Solving Agents that Communicate and Learn

Anjali Narayan-Chen, Colin Graber, Mayukh Das, Md Rakibul Islam, Soham Dan, Sriraam Natarajan, Janardhan Rao Doppa, Julia Hockenmaier, Martha Palmer, Dan Roth

[How to correct problems with metadata yourself]

Abstract
Agents that communicate back and forth with humans to help them execute non-linguistic tasks are a long sought goal of AI. These agents need to translate between utterances and actionable meaning representations that can be interpreted by task-specific problem solvers in a context-dependent manner. They should also be able to learn such actionable interpretations for new predicates on the fly. We define an agent architecture for this scenario and present a series of experiments in the Blocks World domain that illustrate how our architecture supports language learning and problem solving in this domain.
Anthology ID:
W17-2812
Volume:
Proceedings of the First Workshop on Language Grounding for Robotics
Month:
August
Year:
2017
Address:
Vancouver, Canada
Editors:
Mohit Bansal, Cynthia Matuszek, Jacob Andreas, Yoav Artzi, Yonatan Bisk
Venue:
RoboNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
95–103
Language:
URL:
https://aclanthology.org/W17-2812
DOI:
10.18653/v1/W17-2812
Bibkey:
Cite (ACL):
Anjali Narayan-Chen, Colin Graber, Mayukh Das, Md Rakibul Islam, Soham Dan, Sriraam Natarajan, Janardhan Rao Doppa, Julia Hockenmaier, Martha Palmer, and Dan Roth. 2017. Towards Problem Solving Agents that Communicate and Learn. In Proceedings of the First Workshop on Language Grounding for Robotics, pages 95–103, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Towards Problem Solving Agents that Communicate and Learn (Narayan-Chen et al., RoboNLP 2017)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W17-2812.pdf
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