In contrast to abstraction in which an
agent learns the minimal condition list for operators, concept
acquisition refers to the ability of an agent to identify the
discriminating properties of objects in the world, to generate
labels for the objects and to use the labels in the condition list of
operators, thereby associating operations with the concept.
Concept acquisition normally proceeds from a set of positive and
negative instances of some concept (or group of segregated concepts).
With the presentation of the instances, the underlying algorithm
makes correlations between the feature of the instances and their
classification. The problem with this technique as it is described here
is that it requires the specification of both relevant features and
the possible concepts. In general, as an
inductive technique, concept
acquisition should be able to generate new concepts spontaneously and
to recognize the relevant features over the entire input domain.