A Multi-Layered Computational Framework for Enhancing Autonomous Decision-Making in Distributed Computer Systems Using Adaptive Intelligence Models
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V5I1P102Keywords:
Adaptive intelligence, Multi-agent reinforcement learning, Federated online learning, Causal reasoning, Uncertainty quantification, Edge–cloud orchestration, Distributed consensus and robustness, Energy-aware scheduling, Safe/constraint-guided RL, Knowledge graphs and semanticsAbstract
This paper proposes a multi-layered computational framework to enhance autonomous decision-making in distributed computer systems by integrating adaptive intelligence models across the edge–cloud continuum. The framework comprises five tightly coupled layers: (1) a perception and data quality layer that performs streaming ingestion, schema harmonization, and uncertainty-aware feature extraction; (2) a semantic context layer that maintains task-aware knowledge graphs and causal abstractions to support explainable reasoning; (3) an adaptation layer combining online/meta-learning, multi-agent reinforcement learning, and bandit optimization for rapid policy updates under non-stationary workloads; (4) a coordination and trust layer that enforces resilient consensus, incentive-aligned cooperation, and privacy-preserving federation with robustness to adversarial or Byzantine behaviors; and (5) a deployment layer that performs energy- and latency-aware placement, autoscaling, and safe rollback across heterogeneous edge devices and clouds. Novel contributions include (i) a cross-layer feedback mechanism that propagates uncertainty and causal signals to guide exploration–exploitation, (ii) a federated adaptation loop that learns from local experience without moving raw data, and (iii) safety guards that constrain policies via verifiable invariants. We outline evaluation protocols using standardized traces and digital-twin benchmarks, reporting decision quality, tail-latency, throughput, cost/energy per task, fairness, and constraint violations. Use cases industrial IoT, cloud robotics, and cyber-physical infrastructures illustrate how the framework reduces p99 latency and improves policy stability under drift, while retaining interpretability and compliance. The proposed architecture offers a principled path to scalable, trustworthy autonomy in distributed systems
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