Janardhan Rao Doppa


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.


Prune-and-Score: Learning for Greedy Coreference Resolution
Chao Ma | Janardhan Rao Doppa | J. Walker Orr | Prashanth Mannem | Xiaoli Fern | Tom Dietterich | Prasad Tadepalli
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


Learning Rules from Incomplete Examples: A Pragmatic Approach
Janardhan Rao Doppa | Mohammad NasrEsfahani | Mohammad Sorower | Thomas G. Dietterich | Xiaoli Fern | Prasad Tadepalli
Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading