@InProceedings{narayanchen-EtAl:2017:RoboNLP,
  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},
  title     = {Towards Problem Solving Agents that Communicate and Learn},
  booktitle = {Proceedings of the First Workshop on Language Grounding for Robotics},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/W17-2812}
}