The problem of relevance

In chapter 4, Hoftstadter discusses artificial intelligence and the problem of representation. He defines representation as the end product of the process of perception, which is when a set of raw data has been organized into a coherent and structured whole. There are both long-term and short-term representations that hold importance at a given moment during a mental or computational process.

Of course, there are obvious questions as to how this could be implemented in a program so that representations affect a program's decisions in a human-like fashion. For example, a long-term representation could be a heuristic for solving a certain problem, like a crypto problem with 0 as the goal and one of the numbers. However, implementing the representations is not the only problem to deal with.

Constructing representations poses problems because of the problem of relevance and the problem of organization. The problems basically entail deciding what parts of the virtually unlimited number of environmental aspects are to be included in the representation, and how the representation is actually itself represented in the database.

These ideas are interesting to read about because I had wondered how one would develop a program that learns with experience. A program like that would need to have the ability to decide how much to include from its environment, and it would have to know how to represent that data, if it were to create its own representations over time. This is, after all, what people do.