Agent Semantics, Semantic Spacetime, and Graphical Reasoning
By: Mark Burgess
Potential Business Impact:
Helps computers understand and remember information better.
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 $\gamma(3,4)$ 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.
Similar Papers
Theoretical Foundations for Semantic Cognition in Artificial Intelligence
Artificial Intelligence
Builds smarter robots that think and remember.
Self-Organizing Graph Reasoning Evolves into a Critical State for Continuous Discovery Through Structural-Semantic Dynamics
Artificial Intelligence
Helps computers discover new ideas on their own.
A Fully Spectral Neuro-Symbolic Reasoning Architecture with Graph Signal Processing as the Computational Backbone
Artificial Intelligence
Helps computers reason like humans, faster.