( 03rd July 2019 )
A designer of an agent, it is very creative work. It may have a good model of the agent and its environment. Often a designer does not have a good model, and an agent should use data from its past experiences and other sources to help it decide what to do.
The learning dimension determines whether knowledge is given or 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 it.
Sometimes an agent can decide on its best action quickly enough for it to act. Often there are computational resource limits that prevent an agent from carrying out the best action. That is, the agent may not be able to find the best action quickly enough within its memory limitations to act while that action is still the best thing to do.
For example, it may not be much use to take 10 minutes to derive what was the best thing to do 10 minutes ago, when the agent has to act now. Often, instead, an agent must trade off how long it takes to get a solution with how good the solution is; it may be better to find a reasonable solution quickly than to find a better solution later because the world will have changed during the computation.
The computational limits dimension determines whether an agent has perfect rationality, where an agent reasons about the best action without taking into account its limited computational resources; or
bounded rationality, where an agent decides on the best action that it can find given its computational limitations.
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