Rote Learning

Rote Learning is basically memorisation.

• Saving knowledge so it can be used again.
• Retrieval is the only problem.
• No repeated computation, inference or query is necessary.

A simple example of rote learning is caching

• Store computed values (or large piece of data)
• Recall this information when required by computation.
• Significant time savings can be achieved.
• Many AI programs (as well as more general ones) have used caching very effectively.

Memorisation is a key necessity for learning:

• It is a basic necessity for any intelligent program -- is it a separate learning process?
• Memorisation can be a complex subject -- how best to store knowledge?

Samuel's Checkers program employed rote learning (it also used parameter adjustment which will be discussed shortly).

• A minimax search was used to explore the game tree.
• Time constraints do not permit complete searches.
• It records board positions and scores at search ends.
• Now if the same board position arises later in the game the stored value can be recalled and the end effect is that more deeper searched have occurred.

Rote learning is basically a simple process. However it does illustrate some issues that are relevant to more complex learning issues.

Organisation

-- access of the stored value must be faster than it would be to recompute it. Methods such as hashing, indexing and sorting can be employed to enable this.

E.g Samuel's program indexed board positions by noting the number of pieces.

Generalisation

-- The number of potentially stored objects can be very large. We may need to generalise some information to make the problem manageable.

E.g Samuel's program stored game positions only for white to move. Also rotations along diagonals are combined.

Stability of the Environment

-- Rote learning is not very effective in a rapidly changing environment. If the environment does change then we must detect and record exactly what has changed -- the frame problem.

 
• Store v Compute