Structured, factored and atomic representation?

Question

I am currently reading "Artificial Intelligence: A modern Approach". Though the terminology factored, structured and atomic representation is confusing what do these mean exactly?

In relation with programming...

Thanks

Answer 1

I like explanation given by Novak. My 2 cents is to make clarity on difference between factored vs structured. Here is extract from definitions:

• An atomic representation is one in which each state is treated as a black box.
• A factored representation is one in which the states are defined by set of features.
• A structured representation is one in which the states are expressed in form of objects and relations between them. Such knowledge about relations called facts.

Examples:

atomicState == goal: Y/N  // Is goal reached?

It is the only question we can ask to black box.

factoredState{18} == goal{42}: N  // Is goal reached?
diff( goal{42}, factoredState{18}) = 24 // How much is difference?
// some other questions. the more features => more type of questions

The simplest factored state must have at least one feature (of some type), that gives us ability to ask more questions. Normally it defines quantitative difference between states. The example has one feature of integer type.

11grade@schoolA{John(Math=A-), Marry(Music=A+), Job1(doMath)..} == goal{50% ready for jobs}

The key here - structured representation, allows higher level of formal logical reasoning at the search. See First-Order Logic @berkley for introductory info.

This subject easily confuses a practitioner (especially beginner) but makes great sense for comparing different goal searching algorithms. Such "world" state representation classification logically separates algorithms into different classes. It is very useful to draw lines in academic research and compare apples to apples when reasoning academically.

Answer 2

I'm not thrilled with the lines that Russell and Norvig draw, but: Generally, when you're using AI techniques to solve a problem, you're going to have a programmed model of the situation. Atomic/factored/structured is a qualitative measure of how much "internal structure" those models have, from least to most.

Atomic models have no internal structure; the state either does or does not match what you're looking for. In a sliding tile puzzle, for instance, you either have the correct alignment of tiles or you do not.

Factored models have more internal structure, although exactly what will depend on the problem. Typically, you're looking at variables or performance metrics of interest; in a sliding puzzle, this might be a simple heuristic like "number of tiles out of place," or "sum of manhatten distances."

Structured models have still more; again, exactly what depends on the problem, but they're often relations either of components of the model to itself, or components of the model to components of the environment.

It is very easy, especially when looking at very simple problems like the sliding tile, to unconsciously do all the hard intelligence work yourself, at a glance, and forget that your model doesn't have all your insight. For example, if you were to make a program to do a graph search technique on the sliding puzzle, you'd probably make some engine that took as input a puzzle state and an action, and generated a new puzzle state from that. The puzzle states are still atomic, but you the programmer are using a much more detailed model to link those inputs and outputs together.