@inproceedings{narayan-chen-etal-2017-towards,
title = "Towards Problem Solving Agents that Communicate and Learn",
author = "Narayan-Chen, Anjali and
Graber, Colin and
Das, Mayukh and
Islam, Md Rakibul and
Dan, Soham and
Natarajan, Sriraam and
Doppa, Janardhan Rao and
Hockenmaier, Julia and
Palmer, Martha and
Roth, Dan",
editor = "Bansal, Mohit and
Matuszek, Cynthia and
Andreas, Jacob and
Artzi, Yoav and
Bisk, Yonatan",
booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2812",
doi = "10.18653/v1/W17-2812",
pages = "95--103",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Towards Problem Solving Agents that Communicate and Learn
%A Narayan-Chen, Anjali
%A Graber, Colin
%A Das, Mayukh
%A Islam, Md Rakibul
%A Dan, Soham
%A Natarajan, Sriraam
%A Doppa, Janardhan Rao
%A Hockenmaier, Julia
%A Palmer, Martha
%A Roth, Dan
%Y Bansal, Mohit
%Y Matuszek, Cynthia
%Y Andreas, Jacob
%Y Artzi, Yoav
%Y Bisk, Yonatan
%S Proceedings of the First Workshop on Language Grounding for Robotics
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F narayan-chen-etal-2017-towards
%X 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.
%R 10.18653/v1/W17-2812
%U https://aclanthology.org/W17-2812
%U https://doi.org/10.18653/v1/W17-2812
%P 95-103
Markdown (Informal)
[Towards Problem Solving Agents that Communicate and Learn](https://aclanthology.org/W17-2812) (Narayan-Chen et al., RoboNLP 2017)
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.