Scales and Hedges in a Logic with Analogous Semantics
This work addresses a specific issue in cognitive systems and social decision-making by enhancing a logic framework, but it appears incremental as it adds a feature to an existing system without demonstrating broad impact.
The paper tackles the challenge of handling scales for adjective and verb semantics in Context Logic, a two-layered logic with analogous semantics designed to address limitations of classical Fuzzy Logic in forming, grounding, and reasoning with complex formulae. It extends the existing theory by incorporating scales into the system, building on the Activation Bit Vector Machine for logical reasoning.
Logics with analogous semantics, such as Fuzzy Logic, have a number of explanatory and application advantages, the most well-known being the ability to help experts develop control systems. From a cognitive systems perspective, such languages also have the advantage of being grounded in perception. For social decision making in humans, it is vital that logical conclusions about others (cognitive empathy) are grounded in empathic emotion (affective empathy). Classical Fuzzy Logic, however, has several disadvantages: it is not obvious how complex formulae, e.g., the description of events in a text, can be (a) formed, (b) grounded, and (c) used in logical reasoning. The two-layered Context Logic (CL) was designed to address these issue. Formally based on a lattice semantics, like classical Fuzzy Logic, CL also features an analogous semantics for complex fomulae. With the Activation Bit Vector Machine (ABVM), it has a simple and classical logical reasoning mechanism with an inherent imagery process based on the Vector Symbolic Architecture (VSA) model of distributed neuronal processing. This paper adds to the existing theory how scales, as necessary for adjective and verb semantics can be handled by the system.