Adaptive Neuro-Symbolic Planning for Smart Agriculture Microgrid Orchestration

The article explores the use of neuro-symbolic AI and quantum computing to optimize energy distribution for smart agriculture microgrids, which involve renewable energy sources, storage systems, and variable agricultural loads.

Details

The author's journey began with attempting to optimize energy distribution for a small experimental farm using traditional reinforcement learning, but faced challenges with the AI suggesting impractical solutions due to its inability to reason about constraints like equipment maintenance, regulations, and weather forecasts. This led the author to explore neuro-symbolic AI, which combines neural networks' learning capabilities with symbolic AI's reasoning power. The author found that a 'neural frontend with symbolic backend' architecture worked best for planning tasks. Additionally, the author investigated the use of quantum computing for optimization, specifically Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) algorithms, which can enhance classical optimization pipelines even on near-term Noisy Intermediate-Scale Quantum (NISQ) devices. Smart agriculture microgrids represent a complex optimization challenge, balancing objectives like minimizing energy costs, maximizing renewable energy utilization, and meeting operational requirements. The author developed a hybrid architecture integrating neuro-symbolic planning with quantum-enhanced optimization to address these challenges.