Explanation Based Learning (EBL)

Humans appear to learn quite a lot from one example.
Basic idea: Use results from one examples problem solving effort next time around.
An EBL accepts 4 kinds of input:

A training example

-- what the learning sees in the world.

A goal concept

-- a high level description of what the program is supposed to learn.

A operational criterion

-- a description of which concepts are usable.

A domain theory

-- a set of rules that describe relationships between objects and actions in a domain.

From this EBL computes a generalisation of the training example that is sufficient not only to describe the goal concept but also satisfies the operational criterion.
This has two steps:

Explanation

-- the domain theory is used to prune away all unimportant aspects of the training example with respect to the goal concept.

Generalisation

-- the explanation is generalised as far possible while still describing the goal concept.