Burghard B. Rieger:
In: Stamatescu, Ion O. (Ed.): Learning and Adaptivity., Heidelberg/Berlin/New York (Springer) 2003
Human beings appear to be very particular information processing systems whose outstanding plasticity and capability to cope with changing environmental conditions (adaptation) is essentially tied to their use of natural languages in communication to acquire knowledge (learning). Their knowledge based processing of information makes them cognitive, and their sign and symbol generation, manipulation, and understanding capabilities render them semiotic . Semiotic cognitive information processing (SCIP) is inspired by information systems theory according to which living systems process and structure environmental data according to their own structuredness. When these processes are modeled as operating on structures whose representational status is not so much a presupposition to but rather a result from such processing, then the resulting models - being able to simultaneously instanciate, create and/or modify these structures - may attain a quality of sign and symbol understanding which may computationally be realized. This quality will in the sequel be studied and identified as a particular form of knowledge acquisition or learning whose results can be visualized as incremental dynamics of structure formation. Its formal delineation, operational specification, and algorithmic implementation allows for experimental testing of the SCIP system's capability for meaning constitution from natural language texts without prior morphological, lexical, syntactic and/or semantic knowledge.
In response to deficits encountered likewise in computational linguistics (CL), artificial intelligence research (AI) and cognitive psychology (CP) whose theoretical and applicational problems in understanding natural language information processing by men and machine are becoming exceedingly pressing, the last two decades saw a certain renaissance in semiotics. The new interest in the cognitive foundations of sign organization and manipulation processes was spurred even further by artificial life research (AL) and the quest for a principled theory of understanding symbols and languages, models and (re)presentations, simulations and realizations. Such a theory is expected to supply some grounding also for knowledge acquisition as a conception of learning whose formal derivation, procedural instanciation, and testable results provide some symbol and language independent evidence of what can be (said to be) understood.
Following the introductory remarks (1.) will be a short characterization of the cognitive view (2.) to language understanding and the lay-out of a system theoretical frame for cognitive processing (3.), before on the grounds of both the computational semiotics perspective (4.) of memory, knowledge, and understanding is developed. Introducing the functional relevancy of language structures (5.) and their granular decomposition will set the stage for an empirical reconstruction (6.) and the experimental testing (7.) of the (tentative) design and (implemented) modeling of a semiotic cognitive information processing (SCIP) system whose testable language understanding performance is considered an instanciation of enactive learning as summarized in the conclusion (8.).
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