Machine Learning from Verbal Instruction

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Description

Unlike traditional machine learning methods, humans often learn from natural language instruction. As users become increasingly accustomed to interacting with computer devices using speech, their interest in instructing these devices in natural language is likely to grow.

We present our Learning by Instruction Agent (LIA), an intelligent personal agent that users can teach to perform new action sequences to achieve new commands, using solely natural language interaction. LIA uses a CCG semantic parser to ground the semantics of each command in terms of primitive executable procedures defined in terms of the sensors and effectors of the agent. When LIA is given a natural language command that does not understand, it prompts the user to explain how to achieve the command through a sequence of steps, also specified in natural language. As a result of the instruction episode, LIA learns two types of knowledge: (1) a new procedure defined in terms of previously understood commands, and (2) an extension to its natural language lexicon that enables it to understand and execute the new command across multiple contexts (e.g., having been taught how to "forward an email to Alice," LIA can correctly interpret the command "forward this email to Bob." This talk will present LIA, plus ongoing research to extend it to include demonstration-based learning, and theoretical questions raised by such learning from instruction.

background reading: http://ai2-website.s3.amazonaws.com/publications/LearnByInst.pdf

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