this method of teaching seems ideally suited for teaching an Artificial Intelligence.
This is the “20 questions” or “animals” program, which used to be standard in /usr/bin/games on all Unix systems. It isn’t of much value in practice.
That seems like the inverse problem. With the 20 questions game, your AI learns miscellaneous qualities of a whole lot of objects. With this method, your AI learns examples of a quality that it can, if the algorithm works, use to recognize the quality in other examples.
If you’re interested in judging in greater detail how DI might offer any ideas on AI that are both useful and original, the place to start would be Inferred Functions of Performance and Learning (Engelmann and Steely, 2004), which does attempt to set out a theory of learning (and the logically necessary things that must be going on inside of any system that performs a given behavior, whether learned or unlearned).
This is the “20 questions” or “animals” program, which used to be standard in /usr/bin/games on all Unix systems. It isn’t of much value in practice.
That seems like the inverse problem. With the 20 questions game, your AI learns miscellaneous qualities of a whole lot of objects. With this method, your AI learns examples of a quality that it can, if the algorithm works, use to recognize the quality in other examples.
If it works, anyway.
DI is a theory of instruction, not of learning.
If you’re interested in judging in greater detail how DI might offer any ideas on AI that are both useful and original, the place to start would be Inferred Functions of Performance and Learning (Engelmann and Steely, 2004), which does attempt to set out a theory of learning (and the logically necessary things that must be going on inside of any system that performs a given behavior, whether learned or unlearned).
Please see this comment.