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.
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