The Knowledge Society Applications: The RRR Language Machines

The Knowledge Society Applications: The RRR Language Machines

Azamat Abdoullaev (EIS Encyclopedic Intelligent Systems Ltd, Cyprus)
DOI: 10.4018/978-1-59904-966-3.ch012
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Abstract

Like in the human mind marked by rationality, goals, and purposeful actions, knowledge in advanced computer systems and intelligent agents is thought to constitute a distinct level lying above the symbol (computational), the logical (algorithmic), and the physical (biological, mechanical) levels (Newell, 1980; Newell, 1990; Newell, 1993). The symbol or code level with its two kinds of representations, data structures (contents) and processes (codes and procedures), is considered to be a representational medium level realizing knowledge content and actuating the agent’s thoughts and decisions. Accordingly, all inquiries of AI usually are arranged as a separate contribution either to the symbol (code) level or to the knowledge (semantic) level. Any sort of logic, whatever may be its generality, contributes little directly to the knowledge level, since “no matter how general a logic is, it is not at the knowledge level” (Newell, 1980). Logical principles are by nature merely rules of reasoning, deductive arguments, and demonstration, they are not principles of real knowledge. So, being neutral to their subjects, formal logical systems neither describe nor represent, nor explain, nor predict any real phenomena. Consequently, nothing substantive can be deduced from the logical axioms or postulates regarding the nature and order of things in the real world. Nevertheless, despite the public fact that logic says nothing about any real thing, and that it is all about proposition and inference forms, there is an established habit to combine formal logic languages with entity taxonomies and typologies so that to construct a general representational language for the DA, in particular, and knowledge technologies, in general (Ontology and Taxonomies, 2001). Nowadays, such versions of logical formalisms and languages as semantic networks, rule systems, fuzzy logic, frame models, predicate calcuThe lus, or situation calculus still constitute all the thrust of current knowledge and reasoning applications. Some AI practitioners feel concern about this conceptual confusion, and urge on replacing “form-oriented AI research” with “content-directed AI Research,” whereby substituting knowledge engineering with ontological (domain knowledge) engineering, thus, underlining the higher value of content theories in comparison with so-called logical mechanism theories (Mizoguchi, 1998).

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