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.