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|Authors:||MacDonald, Bruce A.|
|Abstract:||Instructable systems - both instructable robots and instructable agents - must acquire skills and knowledge from examples and other instructions easily given by users in factories, laboratories and offices. Both senses of "instruct" are important: command and teach. The human interface must exploit the user's natural instruction abilities and require minimal acquisition of expertise prior to teaching. It is assumed that typical users will not be expert programmers, but will be able to do the tasks they wish to teach and also show them to other humans. Inductive learning techniques are employed to generalize the teacher's examples, in a manner biased by the teacher's other instructions, and thereby form a procedural task description. Instructions can drastically reduce the example and computational complexities of learning problems without compromising learnability. Existing machine learning systems are placed in an instructable framework. Three experimental prototypes are briefly described. Two systems instruct robots: one emphasizing examples and the other emphasizing more explicit instructions. The third is an instructable, office clerk metaphor. Instructability is seen as a small, but significant step toward intelligence.|
|Appears in Collections:||MacDonald, Bruce|
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