foundations of computational agents
Who so neglects learning in his youth, loses the past and is dead for the future.
– Euripides (484 BC – 406 BC), Phrixus, Frag. 927
Learning is the ability of an agent to improve its behavior based on experience. This could mean the following
The range of behaviors is expanded; the agent can do more.
The accuracy on tasks is improved; the agent can do things better.
The speed is improved; the agent can do things faster.
The ability to learn is essential to any intelligent agent. As Euripides pointed out, learning involves an agent remembering its past in a way that is useful for its future.
This chapter considers the problem of making a prediction as supervised learning: given a set of training examples made up of input–output pairs, predict the output of a new example where only the inputs are given. We explore four approaches to learning: choosing a single hypothesis that fits the training examples well, predicting directly from the training examples, selecting the subset of a hypothesis space consistent with the training examples, or (in Section 10.4) predicting based on the posterior probability distribution of hypotheses conditioned on the training examples.