🤖 AI Summary
This study addresses the fragmentation between vectorized learning and knowledge graph representation in cognitive agents, as well as overreliance on large language models and ontology-based modeling. Methodologically, it introduces an ontology-free attention mechanism grounded in commitment theory semantics: (i) a semantic spatiotemporal γ(3,4) graph serves as a unified representational framework, with commitment theory explicitly embedded into attention computation to enable role-driven knowledge categorization; (ii) a causally bounded attention compression paradigm reduces contextual data requirements. Contributions include: (i) the first formal integration of commitment theory into attention semantics, enabling causal reasoning under uncertainty; (ii) order-of-magnitude context compression in autonomous robotics, defense, and emergency response applications—while preserving statistical stability, source-intent fidelity, and real-time inference robustness.
📝 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 $γ(3,4)$ 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.