Agent Semantics, Semantic Spacetime, and Graphical Reasoning
This work addresses foundational issues in knowledge representation and process modeling for AI and semantic reasoning, but appears incremental as it builds on existing Promise Theory.
The paper tackles the problem of information leakage in graph processes due to absorbing states, proposing the Semantic Spacetime model to clarify how intentionality can address these issues through boundary information.
Some formal aspects of the Semantic Spacetime graph model are presented, with reference to its use for directed knowledge representations and process modelling. A finite $γ(3,4)$ representation is defined to form a closed set of operations that can scale to any degree of semantic complexity. The Semantic Spacetime postulates bring predictability with minimal constraints to pathways in graphs. The ubiquitous appearance of absorbing states in any partial graph means that a graph process leaks information. The issue is closely associated with the issue of division by zero, which signals a loss of closure and the need for manual injection of remedial information. The Semantic Spacetime model (and its Promise Theory) origins help to clarify how such absorbing states are associated with boundary information where intentionality can enter.