A Scalable Artificial Intelligence Framework for Autonomous Decision Making and Knowledge Representation in Complex Computational Systems

The rapid digitization of enterprise, healthcare, and industrial processes has created an urgent demand for artificial intelligence systems capable of autonomous, transparent, and scalable decision making across highly complex computational environments. Conventional AI decision support systems suffer from three fundamental limitations: opacity of inference processes that undermines stakeholder trust, degraded performance under distributional shift when deployed at scale, and inadequate mechanisms for structured knowledge representation that would allow AI conclusions to be audited, corrected, and incrementally refined. These shortcomings prevent organizations from fully leveraging AI-driven automation in high-stakes domains where accountability is non-negotiable. This research proposes the Scalable Artificial Intelligence Framework (SAIF), a novel architecture that integrates autonomous decision making, dynamic knowledge representation, and built-in explainability into a unified computational system designed to operate reliably at organizational scale. SAIF is grounded in three theoretical pillars: (i) a hybrid neuro-symbolic reasoning engine that combines the pattern recognition strengths of deep neural networks with the structural clarity of symbolic logic, (ii) an adaptive ontology management system that continuously updates its knowledge graph using information extracted from operational decisions and environmental feedback, and (iii) a Transparency and Trust Engine that produces human-interpretable explanations calibrated to the cognitive needs of diverse stakeholders. Three algorithms are central to SAIF's operation: the Adaptive Knowledge Graph Evolution Algorithm (AKGEA) for continuous ontology refinement, the Multi-Objective Autonomous Decision Algorithm (MOADA) for Pareto-optimal decision synthesis under conflicting constraints, and the Explainability-Aware Representation Learning algorithm (EARL) for producing compact, interpretable model representations. Experimental evaluation across benchmark datasets and simulated enterprise scenarios demonstrates that SAIF achieves 96.1% decision accuracy on large datasets, sustains 24.6 decisions per second under 5,000 concurrent users, and maintains 52.9% explainability at the highest complexity level tested, representing improvements of 9.7%, 203.7%, and 110.7% respectively over the strongest baseline. Memory utilization under knowledge base growth was reduced by 43.7% compared to competing frameworks. These results establish SAIF as a viable foundation for enterprise-grade autonomous decision systems that honor the transparency requirements of modern regulatory frameworks while delivering the performance demanded by large-scale deployment environments. The framework advances the state of the art in AI-driven decision support and opens new research directions in adaptive knowledge management and human-AI collaborative intelligence.