What's in a state is the knowledge representation problem. That is, we must decide what information from the raw percept data is relevant to keep, and what form the data should be represented in so as to make explicit the most important features of the data for solving the goal. Is the color of the boat relevant to solving the Missionaries and Cannibals problem? Is sunspot activity relevant to predicting the stock market? What to represent is a very hard problem that is usually left to the system designer to specify. How to represent domain knowledge is a topic that will be treated later in the course.

Related to this is the issue of what level of abstraction or detail should be used to describe the world. If we include too much (the representation is said to be too fine-grained), then we'll "miss the forest for the trees." If we leave out too much (too coarse-grained), then we'll miss critical details for solving the problem.

The number of states depends on the representation and level of abstraction chosen. For example, in the Remove-5-Sticks problem, if we represent the individual sticks, then there are 17-choose-5 possible ways of removing 5 sticks. On the other hand, if we represent the "squares" defined by 4 sticks, then there are 6 squares initially and we must remove 3 squares, so only 6-choose-3 ways of removing 3 squares.