TAG ARCHIVE

vector-space

1 MARIA OS blog articles tagged vector-space, organized as a Bonginkan topic archive for search engines and LLM retrieval.

1 article|Published by Bonginkan

Judgment OS / Decision Intelligence OS

Core MARIA OS research on turning organizational judgment into executable decision systems.

Responsibility Gates and AI Governance

Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents.

Multi-Agent Mathematics

Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation.

Evidence, RAG, and Knowledge Governance

Evidence bundles, retrieval architecture, Graph RAG, knowledge trust, and auditable reasoning pipelines.

MathematicsFebruary 14, 202648 min read

Knowledge Graph Embedding for Agent Competence Assessment: Translational Distance Models in Responsibility Space

Mapping agents, decisions, and outcomes into continuous vector spaces to quantify competence through translational-distance geometry

Assessing AI-agent competence in enterprise governance requires moving beyond binary success/failure metrics toward a continuous, context-sensitive model. This paper introduces a knowledge-graph-embedding framework based on translational-distance models (TransE, RotatE) adapted to the MARIA OS responsibility space. Agents, decisions, and outcomes are embedded in a shared vector space, where competence is measured by distance between context-translated agent embeddings and ideal outcome embeddings. We formalize the geometry, derive governance-aware loss functions, analyze convergence behavior, and show that KGE-derived competence scores correlate with held-out success probability at r = 0.89.

knowledge-graphembeddingsagent-competenceTransEresponsibility-spacevector-spacecompetence-assessment