Burghard B. Rieger
On Understanding Understanding.
Perception-Based Processing of NL Texts in SCIP Systems, or Meaning Constitution as
Visualized Learning
In: Artés, A. / Fernández-Villacañas Martin, J.-L (eds.):
Learning: Advances in Multimedia Communications, Information Processing, and Education.
[IEEE Transactions on System, Man, and Cybernetics, Part C: Vol.34, No.4] 2004, pp. 425-438
Abstract
Inspired by information systems theory, Semiotic Cognitive Information Processing (SCIP) is
grounded in (natural/artificial) system-environment situations. SCIP systems' knowledgebased
processing of information makes it cognitive, their sign and symbol generation,
manipulation, and understanding capabilities render it semiotic. Based upon structures
whose representational status is not a presupposition to, but a result from recursive processing,
SCIP algorithms initiate and modify the structures they are operating on to realize (rather than
simulate) language understanding by meaning constitution. Thus, the symbolic (de)composition
of propositional structures in traditional semantics is complemented by SCIP, which models
learning and understanding dynamically by visualizing what is understood in a
perception-based, sub-symbolic, multi-resolutional way of processing natural language
discourse. An experimental 2-dim scenario with object locations described relative to a
mobile agent's varying positions allows to test SCIP systems' performance against human
natural language understanding in a controlled way.
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