Abstract
The symbol-based, correspondence epistemology used in AI is
contrasted with the constructivist, coherence epistemology promoted by cybernetics.
The latter leads to bootstrapping knowledge representations, in which different parts
of the cognitive system mutually support each other. Gordon Pask's entailment
meshes and their implementation in the THOUGHTSTICKER program are reviewed
as a basic application of this approach. Entailment meshes are then extended to
entailment nets: directed graph representations governed by the “bootstrapping
axiom”, determining which concepts are to be distinguished or merged. This allows
a constant restructuring and elicitation of the conceptual network. Semantic networks
and frame-like representations with inheritance can be expressed in this very general
scheme by introducing a basic ontology of node and link types. Entailment nets are
then generalized to associative nets characterized by weighted links. Learning
algorithms are presented which can adapt the link strengths, based on the frequency
with which links are selected by hypertext browsers. It is argued that these different
bootstrapping methods could be applied to make the World-Wide Web more
intelligent, by allowing it to self-organize and support inferences through spreading
activation.
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