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
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
- 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)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/W17-2812.pdf