KoAdaption
KoAdaption is a theoretical framework for adaptive knowledge-driven systems that aim to modify their behavior in response to changing environments while maintaining a coherent knowledge base. The term combines knowledge and adaptation, emphasizing the integration of structured information with dynamic adjustment mechanisms.
It rests on a knowledge representation layer that encodes domain concepts, a context detector that monitors
Techniques used within KoAdaption include rule-based reasoning, probabilistic inference, and machine learning components that update rules,
Potential applications span robotics, education technology, personalized content delivery, and operational domains such as network management
Key challenges include maintaining consistency of the knowledge base during updates, avoiding conflicting adaptations, addressing data
KoAdaption has circulated mainly in theoretical and cross-disciplinary discussions of adaptive AI and knowledge-based systems and