Semiotic Modeling

Origin. Soviet semiotics applied to artificial intelligence, 1970s-1980s. Key contributors include Yuri Shreider, Mikhail Bongard, and researchers connected to the Moscow-Tartu semiotic school. Dmitry Pospelov integrated semiotic concepts into the Soviet AI program. Built on Lotman's cultural semiotics and Peirce's sign theory.

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

Procedure. Design systems that handle meaning as context-dependent interpretation: (1) Identify sign systems — what sign systems are relevant to the domain? Formal languages, natural language, diagrams, icons, gestures. Each has its own rules of formation and interpretation. (2) Model interpretation range — for each sign type, what interpretations are possible? What contextual factors select among them? The same sign may mean different things in different contexts. (3) Model the interpreter — what signs can the interpreter recognize? What interpretations are available to them? What background knowledge and context do they bring? Different interpreters construct different meanings from the same signs. (4) Analyze communication conditions — successful communication requires that sender's intended meaning matches receiver's interpretation. Identify what must be shared (common ground, conventions, context) for alignment. (5) Design for robustness — anticipate misinterpretation. Provide redundancy, feedback mechanisms, and repair strategies for when interpretation fails. (6) Test with diverse interpreters — different users bring different contexts; test with the range of expected interpreters.

Applies to. Human-machine communication. Natural language understanding. Interface design. Design of representational systems. Any domain where meaning is context-dependent and interpretation varies.

Limitations. Semiotic models can become unfalsifiably flexible: any misunderstanding can be attributed to context or interpreter difference. The models are descriptive rather than predictive — they explain interpretation but do not reliably predict specific interpretations. The computational implementation of semiotic concepts was never fully achieved; the framework remained more philosophical than algorithmic. Integration with formal methods is difficult.