AAAI Publications, Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence

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Learning to Interpret Natural Language Instructions
James MacGlashan, Monica Babes-Vroman, Kevin Winner, Ruoyuan Gao, Richard Adjogah, Marie desJardins, Michael Littman, Smaranda Muresan

Last modified: 2012-07-15


We address the problem of training an artificial agent to follow verbal commands using a set of instructions paired with demonstration traces of appropriate behavior. From this data, a mapping from instructions to tasks is learned, enabling the agent to carry out new instructions in novel environments. Our system consists of three components: semantic parsing (SP), inverse reinforcement learning (IRL), and task abstraction (TA). SP parses sentences into logical form representations, but when learning begins, the domain/task specific meanings of these representations are unknown. IRL takes demonstration traces and determines the likely reward functions that gave rise to these traces, defined over a set of provided features. TA combines results from SP and IRL over a set of training instances to create abstract goal definitions of tasks. TA also provides SP domain specific meanings for its logical forms and provides IRL the set of task-relevant features.


reinforcement learning; natural language processing; grounding language; robots; task abstraction

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