Ontology of Communication. Agent-Based Data-Driven or Sign-Based Substitution-Driven?

Database Semantics (DBS, AIJ’89) is an agent-based data-driven theory of how natural language communication works. The prototype resembles natural agents in that it assumes (i) real bodies out there in the real world (embodiment, MacWhinney 2008) and (ii) an agent-internal cognition which includes an interface component for elementary recognition and action, a memory component for storing continuous monitoring,
and an operations component for building and processing content.
In language communication, DBS agents switch between the speak and the hear mode (turn-taking, Sacks et al. 1974, Schegloff 2007). The speak mode is driven by navigating along the semantic relations in a cognition-internal content (input) resulting in cognition-external raw data (output), e.g. sound waves or pixels, which have
no meaning or grammatical properties whatsoever but may be measured by natural-science. The hear mode is driven by the raw data (input) produced by the speaker resulting in cognition-internal content (output). For communication to be successful, the content encoded by the speaker into raw data and the content decoded by the hearer from those raw data must be the same (minimal requirement).
Contents are built in agent-internal cognition from the classical semantic kinds concept, indexical, and name, and connected with the classical semantic relations of functor-argument (Chapter 4) and coordination (Chapter 5). The interaction between the agent-internal cognition and the agent-external raw data is based on the computational Mechanisms of (i) type-token matching for concepts, (ii) pointing at values of the on-board orientation system for indexicals, and implicit or explicit (iii) baptism for named referents. The computational complexity of natural language communication in DBS is linear (TCS’92).