Precisiated natural language - toward a radical enlargement of the role of natural languages in information processing, decision and control

It is a deep-seated tradition in science to view the use of natural languages in scientific theories as a manifestation of mathematical immaturity. The rationale for this tradition is that natural languages are lacking in precision. In a related way, the restricted expressive power of predicate-logic-based languages rules out the possibility of defining many basic concepts such as causality, resemblance, smoothness and relevance in realistic terms. In this instance, as in many others, the price of precision is over-idealization and lack of robustness. In a significant departure from existing methods, in the approach which is described in this talk the high expressive power of natural languages is harnessed by constructing what is called a precisiated natural language (PNL). In essence, PNL is a subset of a natural language (NL): a subset which is equipped with constraint-centered semantics (CSNL) and is translatable into what is called the Generalized Constraint Language (GCL).