In the pantheon of Artificial Intelligence literature, few concepts are as intriguing—and as misunderstood—as the early attempts to model "understanding" inside a machine. Before the era of deep learning, big data, and neural networks, there was a quieter, more philosophical battle: Could a computer truly understand language, stories, and scenarios, or was it merely manipulating symbols?

QUALM introduced the notion of goal trees and plan recognition . Modern recommender systems and virtual assistants still use variations of this architecture.