1 March 1992 Task learning from instruction: an application of discourse processing techniques to machine learning
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Abstract
A good teacher will provide crucial information about a new task, rather than simply performing examples with no elaboration. Machine learning paradigms have ignored this form of instruction, concentrating on induction over multiple examples, or knowledge-based generalization. This paper presents a model of supervised task learning designed to exploit communicative acts. Instruction is viewed as planned explanation, and plan recognition is applied to the problem at both domain and discourse levels, and extended to allow the learner to have incomplete knowledge. The model includes a domain level plan recognizer and a discourse level plan recognizer that cues a third level of plan structure rewriting rules. The rewriter may add new domain operator schemata. Details are given of an example in which a robot apprentice is instructed in the building of arches.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John D. Lewis, John D. Lewis, Bruce Alexander MacDonald, Bruce Alexander MacDonald, } "Task learning from instruction: an application of discourse processing techniques to machine learning", Proc. SPIE 1707, Applications of Artificial Intelligence X: Knowledge-Based Systems, (1 March 1992); doi: 10.1117/12.56883; https://doi.org/10.1117/12.56883
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