Dynamic Knowledge Representation Strategies for Autonomous Learning in Large-Scale Computational Networks
Keywords:
Dynamic knowledge representation, autonomous learning, computational networks, ontology-based models, neural-symbolic integration, large-scale systemsAbstract
The increasing complexity and dynamism of large-scale computational networks necessitate innovative approaches for knowledge representation that support real-time autonomous learning. Traditional static models are insufficient for addressing the fluidity and scale of data interactions, especially within decentralized and self-evolving systems. This paper explores the development and integration of dynamic knowledge representation strategies that enhance learning capabilities in distributed computational environments. Emphasis is placed on adaptive graph structures, ontology-based frameworks, and neural-symbolic systems capable of handling real-time data updates and feedback mechanisms. We present a conceptual model combining these strategies and evaluate its potential for enabling efficient, scalable, and resilient autonomous learning. Our findings suggest that such dynamic approaches not only improve learning accuracy and adaptability but also offer significant advantages in fault tolerance, interoperability, and self-optimization.
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