Inductive Learning

This involves the process of learning by example -- where a system tries to induce a general rule from a set of observed instances.
This involves classification -- assigning, to a particular input, the name of a class to which it belongs. Classification is important to many problem solving tasks.
A learning system has to be capable of evolving its own class descriptions:

• Initial class definitions may not be adequate.
• The world may not be well understood or rapidly changing.

The task of constructing class definitions is called induction or concept learning

A Blocks World Learning Example -- Winston (1975)

• The goal is to construct representation of the definitions of concepts in this domain.
• Concepts such a house - brick (rectangular block) with a wedge (triangular block) suitably placed on top of it, tent - 2 wedges touching side by side, or an arch - two non-touching bricks supporting a third wedge or brick, were learned.
• The idea of near miss objects -- similar to actual instances was introduced.
• Input was a line drawing of a blocks world structure.
• Input processed (see VISION Sections later) to produce a semantic net representation of the structural description of the object (Fig. 27)  
  Fig. 27 House object and semantic net
• Links in network include left-of, right-of, does-not-marry, supported-by, has-part, and isa.
• The marry relation is important -- two objects with a common touching edge are said to marry. Marrying is assumed unless does-not-marry stated.

There are three basic steps to the problem of concept formulation:

1. Select one know instance of the concept. Call this the concept definition.
2. Examine definitions of other known instance of the concept. Generalise the definition to include them.
3. Examine descriptions of near misses. Restrict the definition to exclude these.

Both steps 2 and 3 rely on comparison and both similarities and differences need to be identified.