View source for Artificial Intelligence/Agent Learning

An agent's ability to learn is essential for unknown environments. Learning exposes the agent to reality, rather than a human attempting to define and write the reality down.

Learning is meant to modify an agent's behavior, such that it improves performance. However, implementation depends on:
* What is already known by the agent/KB.
* How performance is evaluated.
* What function is to be adjusted by learning.
* <Check notes, missing data>.


== Learning Types ==

* '''Inductive Learning''' - Uses a basic function or rule which maps input to output.
** This tends to be one of the simplest learning implementations.
* '''Supervised Learning''' - Has a dataset of the "correct" output. Trains on this dataset, trying to learn what the correct output is.
* '''Unsupervised Learning''' - Learns the "correct" output over time without being given any answers.
* '''Reinforcement Learning''' - Learns from a series of rewards and punishments.