In order to learn a decision tree, our program will need to have some information to learn from: a training set of examples, each of which gives its particular value for the problem attributes in a specific situation.
In the restaurant example, our problem attributes are "What is the estimated time?, "What kind of food do they serve?", and the like. The target attribute is "Will we wait?" It is a boolean attribute: its value is either yes or no.
Problems with boolean target attributes are called classification problems. The learning agent is learning to recognize whether a situation is a positive example of some concept or a negative example.
... as time permits.