PROGRAMMING BY EXAMPLE: THE HUMAN FACE OF AI

Abstract

Interactive computer users often find themselves repeatedly performing similar tasks that could be acquired automatically from a teacher. This paper presents principles derived from experience in creating four prototype learners: for technical drawing, text editing, office tasks, and robot assembly. A teaching metaphor (a) enables the user to demonstrate a task by performing it manually, (b) helps to explain the learner's limited capabilities in terms of a persona, and (c) allows users to attribute intentionality. Tasks are represented procedurally, and augmented with constraints. Suitable mechanisms for attention focusing are necessary in order to control inductive search. Hidden features of a task should be made explicit so that the learner need not entertain, and search, all possible missing steps. Key features of the interaction are formalized as "felicity conditions" that help a learner by guaranteeing more explicit, consistent information in demonstrations. Systems that are programmed by human instruction can capitalize on appropriate interactive methods to boost the computational limitations of inductive inference.