Smart Decision Orchestration for Consumer Electronics Management Using Dynamic Neuro-Symbolic AI Fusion

Existing deep learning techniques applied for consumer electronics fall short regarding transparent decisionmaking capabilities because they lack generality, efficiency, or transparency. This study presents Neuro-Symbolic Manifold Encoding and Causal Reinforcement Learning (NSMECRL), which introduces a novel framework that integrates the technical process of Differentiable Symbolic Logic Encoding (DSLE) with Geometric Neural Manifold Embeddings (GNME) along with Stochastic Causal Relational Inference (SCRI) to address existing problems. For semantic reasoning, DSLE employs algebraictopological rules and tensor decomposition; GNME applies geometric embeddings to simplify user-device relationships, reducing computational complexity by 42.7%; SCRI integrates variational autoencoders with Bayesian graphs, enhancing anomaly detection accuracy by 51.3%. In addition, the framework utilizes a Heterogeneous Neuro-Symbolic Reinforcement Policy (HNSRP) that performs dynamic learning adjustments through which decision reliability increases by 47.6%. The NSMECRL approach attained 93.2% achievement in context-aware decisionmaking with 58.9% increased adaptability capability alongside 55.4% improved explainability, thus becoming an advanced intelligent AI solution for future consumer electronics applications.