Abstract
Database Semantics (DBS) models the cycle of natural language communication as a transition from the hear to the think to the speak and back to the hear mode (turn taking). In contradistinction to the substitution-driven sign-based approaches of truth-conditional semantics and phrase structure grammar, DBS is data-driven and agent-based. The purpose is a theory of semantics for an autonomous robot with language.
Propositions are content in DBS, instead of denoting truth values (Sects. 1–3). Content is built from the semantic kinds of referent, property, and relation, which are concatenated by the classical semantic relations of structure, i.e. functor-argument and coordination. To enable reference as an agent-internal cognitive process, language and nonlanguage contents use the same computational data structure and operation kinds, and differ mostly in the presence vs. absence of language-dependent surface values.
DBS consists of (i) an interface, (ii) a memory, and (iii) an operation component. (The components correspond roughly to those of a von Neumann machine (Neumann 1945): the (i) interface component corresponds to the vNm input-output device, the (ii) memory (database) component corresponds to the vNm memory, and the (iii) operation component performs functions of the vNm arithmetic-logic unit.) The interface component mediates between the agent’s cognition and its external and internal environment, represented as raw data provided by sensors and activators (Sects. 4–7). The data of the agent’s moment by moment monitoring are stored at the memory’s now front. As part of the on-board control unit, the now front is the location for performing the procedures of the operation component, resulting in content.
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Notes
- 1.
The type-token distinction was introduced by C. S. Peirce (CP 4:537).
- 2.
The terminology of member proplets and owner values is reminiscent of the member and owner records in a classic network database (Elmasri and Navathe 1989\(^{1}\)–2017\(^{7}\)), which inspired the database schema of the A-memory in DBS.
- 3.
Because the Xfig graphics editor used here does not provide a satisfactory representation of arrows in the linear notation of speak mode operation names, the arrow heads are omitted in the (iv) surface realization. Nevertheless, the direction of a traversal is specified unambiguously by the arc number written directly above in the top line. For further detail see NLC 6.1.4 ff.; CLaTR Chap. 7.
- 4.
The operations of arithmetic as they are processed by the human brain are described by Menon (2011).
- 5.
- 6.
For an early overview see Benson (1994).
- 7.
Reimer and Michaelson (2014) extend the referring part from language to “representational tokens,” which include cave paintings, pantomime, photographs, videos, etc. DBS goes further by generalizing the referring part to content per se, i.e. without the need for any external representation.
- 8.
Newell and Simon call the agent’s external surroundings the task environment (Newell and Simon 1972).
- 9.
The [±surface] and [±external] distinctions are not available in truth-conditional semantics and generative grammar because their sign-based ontology provides neither for cognition nor for cognitive modes.
- 10.
On the phone, the speaker may use an immediate reference which is mediated for the hearer and vice versa. For example, if the speaker explains to the hearer where to find something in the speaker’s apartment, the speaker uses mediated reference and the hearer immediate reference.
- 11.
Aho and Ullman (1977), p. 47; FoCL 10.1.1.
- 12.
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Hausser, R. (2020). Database Semantics for Talking Autonomous Robots. In: Kurosu, M. (eds) Human-Computer Interaction. Multimodal and Natural Interaction. HCII 2020. Lecture Notes in Computer Science(), vol 12182. Springer, Cham. https://doi.org/10.1007/978-3-030-49062-1_43
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