$\gamma(3,4)$ `Attention' in Cognitive Agents: Ontology-Free Knowledge Representations With Promise Theoretic Semantics

TL;DR

This paper introduces a novel approach combining promise theoretic semantics with vectorized and graph-based knowledge representations to improve reasoning under uncertainty in autonomous agents.

Contribution

It presents an ontology-free semantic framework using $ ext{γ}(3,4)$ graphs that bridges machine learning and knowledge graphs without relying on language models.

Findings

Semantic spacetime graphs enable reasoning without complex ontologies.

Attention mechanisms improve data compression for autonomous reasoning.

Knowledge and learning networks can coexist to handle different data aspects.

Abstract

The semantics and dynamics of `attention' are closely related to promise theoretic notions developed for autonomous agents and can thus easily be written down in promise framework. In this way one may establish a bridge between vectorized Machine Learning and Knowledge Graph representations without relying on language models implicitly. Our expectations for knowledge presume a degree of statistical stability, i.e. average invariance under repeated observation, or `trust' in the data. Both learning networks and knowledge graph representations can meaningfully coexist to preserve different aspects of data. While vectorized data are useful for probabilistic estimation, graphs preserve the intentionality of the source even under data fractionation. Using a Semantic Spacetime $\gamma(3,4)$ graph, one avoids complex ontologies in favour of classification of features by their roles in semantic processes. The latter favours an approach to reasoning under conditions of uncertainty. Appropriate attention to causal boundary conditions may lead to orders of magnitude compression of data required for such context determination, as required in the contexts of autonomous robotics, defence deployments, and ad hoc emergency services.