adaptive classifiers


Many information search, classification, and extraction problems are variations of a single problem in which:

* each item in a potentially infinite stream of items must be classified,
* that classification must be made before the next item is seen,
* that classification must be made based largely on implicit information provided by a teacher's behavior rather than explicit guidance,
* incorrect classifications are allowed as long as asymptotic behavior is acceptable,
* no classification can take arbitrarily long, nor use arbitrary amounts of computation.

Here are several instances of this general problem:

* Arrange mail or news articles into articles a user is likely to want to see and articles the user is not likely to want to see.
* Find webpages that a user is likely to want to see.
* A user is searching for a hard to describe image in a large image database; given that the user is willing to implicitly grade the closeness of other images to it, help the user find the image.
* A user just gave the following command or page reference to an operating system, graphical interface, or application program, but it's not quite correct, figure out which one was meant.
* A game player used a particular rule to generate all of the following phenomena; guess the rule.
* I've watched all these movies (listened to all these singers, eaten all these foods, read all these books, seen all these paintings, driven all these cars, rented all these apartments, visited all these cities, dated all these people) and these had property X while those didn't, find new things likely to have property X.

Constructing a computer program general enough to be profitably used for each of these variations with relatively small modification per variation is the information helper problem.

The Information Helper Problem
Three Stage Development
A Science Analogy
Programming As Hinting
Hinting on Blackboards
An Example of Agent Science
Living Architecture
Intuition-Style Programming

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