@inproceedings{02b06b70bbdb4693b527a21423d7030c,
title = "Towards problem solving agents that communicate and learn",
abstract = "Agents that communicate back and forth with humans to help them execute nonlinguistic 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 contextdependent 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.",
author = "Anjali Narayan-Chen and Colin Graber and Mayukh Das and Islam, {Md Rakibul} and Soham Dan and Sriraam Natarajan and Doppa, {Janardhan Rao} and Julia Hockenmaier and Martha Palmer and Dan Roth",
note = "Publisher Copyright: {\textcopyright} 2017 Proceedings of the 1st Workshop on Language Grounding for Robotics, RoboNLP 2017 at the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017. All rights reserved.; 1st Workshop on Language Grounding for Robotics, RoboNLP 2017 at the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 ; Conference date: 03-08-2017",
year = "2017",
language = "English (US)",
series = "Proceedings of the 1st Workshop on Language Grounding for Robotics, RoboNLP 2017 at the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017",
publisher = "Association for Computational Linguistics (ACL)",
pages = "95--103",
editor = "Mohit Bansal and Cynthia Matuszek and Jacob Andreas and Yoav Artzi and Yonatan Bisk",
booktitle = "Proceedings of the 1st Workshop on Language Grounding for Robotics, RoboNLP 2017 at the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017",
}