Inductive Learning and Concept Acquisition

In contrast to abstraction, 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.

Architectures having this capability include:

Go to A List of Common Capabilities.

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