Instantiation Based

Instantiation-based methods are gaining traction across various AI subfields, focusing on efficiently selecting and utilizing specific examples (instances) to solve complex problems or improve model performance. Current research explores instantiation in diverse contexts, including automated theorem proving, resource-aware tool selection, and commonsense reasoning, often employing machine learning techniques like graph neural networks and recurrent neural networks to guide instance selection or generate relevant exemplars. These advancements are significant because they address limitations of existing methods that struggle with scalability, cost-effectiveness, or the handling of novel or exceptional cases, ultimately leading to more robust and efficient AI systems.