Semiotic Modeling

Origin. Soviet semiotics applied to AI; work by Yuri Shreider, Mikhail Bongard, and the Moscow-Tartu semiotic school; Pospelov's integration of semiotics into AI.

Mechanism. Models intelligent behavior as sign processing, not symbol manipulation. Signs have three components: the signifier (physical form), the signified (meaning), and the interpretant (the effect on the interpreter). Understanding is not pattern matching but interpretation in context. The same sign can have different meanings to different interpreters or in different contexts. Semiotic AI emphasizes the interpreter's model and context-dependence.

Procedure. Identify the sign systems relevant to the domain — formal languages, natural language, diagrams, gestures. For each sign, define the range of possible interpretations and the contextual factors that select among them. Model the interpreter's knowledge state: what signs can they recognize, what interpretations are available to them, what context do they bring? Communication succeeds when the sender's intended meaning matches the receiver's interpretation; model the conditions for this alignment.

Applies to. Human-machine communication, natural language understanding, design of representational systems, any domain where meaning is context-dependent.

Limitations. Semiotic models can become unfalsifiably flexible; any misunderstanding can be attributed to context or interpreter difference. The models are descriptive rather than predictive. Also: the computational implementation of semiotic concepts was never fully realized; the framework remained more philosophical than algorithmic.