Colin Graber
2017
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
Proceedings of the First Workshop on Language Grounding for Robotics
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.
Search
Co-authors
- Anjali Narayan-Chen 1
- Mayukh Das 1
- Md Rakibul Islam 1
- Soham Dan 1
- Sriraam Natarajan 1
- show all...