Neuro-Symbolic Programming with Adaptive Rule Induction

This paper introduces a novel neuro-symbolic programming framework designed to overcome the limitations of traditional hybrid systems that heavily rely on manually defined rules. The core innovation lies in an adaptive rule induction algorithm where a neural network actively learns to generate and refine symbolic rules based on observed data. This creates a symbiotic relationship between neural and symbolic computation, enhancing adaptability and generalizability. The system employs a dynamic rule set adjustment mechanism, responding to performance metrics and uncertainty estimates. We demonstrate the potential of this approach through a theoretical framework outlining the key components and operational principles. This work addresses the critical need for more flexible and robust neuro-symbolic systems, paving the way for intelligent systems capable of learning and reasoning in complex, evolving environments. The system utilizes a recurrent neural network (RNN) to process data and a symbolic rule engine to execute and refine the learned rules. The core of the system is the adaptive rule induction algorithm, which governs the evolution of the symbolic rule set.