Towards Meaning Processes in Computers from Peircean Semiotics
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In this work, we propose a computational approach to the triadic model of semiosis (meaning processes), as stated by Charles Sanders Peirce. His model of semiosis represents a key element in the construction of the next generation of intelligent artifacts able to overcome current AI limitations, such as the symbol grounding problem. The contributions of the Peircean semiotics to the construction of intelligent systems were not yet systematically explored. In fact, most approaches in the literature adopt a naïve definition of semiosis, which usually plays a secondary role in the study. Our research, on the other hand, strives for a strong theoretical foundation for meaning processes, as well as its realization within digital computers. We investigate theoretical constraints about the feasibility of simulated semiosis. These constraints, which are basic requirements for the simulation of semiosis, refer to the synthesis of irreducible triadic relations (Sign – Object – Interpretant). We examine the internal organization of the triad, that is, the relative position of its elements and how they relate to each other by determinative relations. We also suggest a multi-level (micro and macro-semiosis) computational approach based on self-organization principles. In this context, relations of determination are described as emergent properties of the system.
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