Artificial Intelligence Foundations of Computational Agents

Learning is the ability of an agent to improve its behavior based on experience. This could mean the following

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 (page 512)) predicting based on the posterior probability distribution of hypotheses conditioned on the training examples.

Chapter 10 considers learning probabilistic models. Chapter 12 covers reinforcement learning. Section 15.2 (page 701) considers learning relational representations.

Learning Issues

The following components are part of any learning problem:

Task The behavior or task that is being improved

Data The experiences that are used to improve performance in the task, usually in the form of a sequence of examples

Measure of improvement How the improvement is measured – for example, new skills that were not present initially, increasing accuracy in prediction, or improved speed

Consider the agent internals of Figure 2.9 (page 66). The problem of learning is to take in prior knowledge and data (e.g., about the experiences of the agent) and to create an internal representation (the knowledge base) that is used by the agent as it acts.

Learning techniques face the following issues:

Task Virtually any task for which an agent can get data or experiences can be learned.