In some cases, a designer of an agent may have a good model of the agent and its environment. But often a designer does not have a good model, and so an agent should use data from its past experiences and other sources to help it decide what to do.

The learning dimension determines whether

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  knowledge is given, or

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  knowledge is learned (from data or past experience).

Learning typically means finding the best model that fits the data. Sometimes this is as simple as tuning a fixed set of parameters, but it can also mean choosing the best representation out of a class of representations. Learning is a huge field in itself but does not stand in isolation from the rest of AI. There are many issues beyond fitting data, including how to incorporate background knowledge, what data to collect, how to represent the data and the resulting representations, what learning biases are appropriate, and how the learned knowledge can be used to affect how the agent acts.

Learning is considered in Chapters 7, 10, 12, and 15.