אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

The script theory in psychology suggests that cognitive scripts, sequences of expected actions in commonly encountered situations, play a significant role in shaping our comprehension of the world. The concept of scripts was utilized in artificial intelligence in its early days, serving as a tool for representing procedural knowledge and enhancing story understanding. Script-based methods, like the Script Applier Mechanism (SAM), marked a significant advancement in AI, but their reliance on manually crafted rules posed scalability issues that led to the discontinuation of their use.

In this seminar, we introduce a novel method that builds upon the concept of scripts to automatically create explicit schemes of event chains, which we refer to as Generalized Narratives (GNs). Our method uses knowledge graphs to transform sequences of events into GNs, creating a more abstract and generalized representation of events. This approach is completely explicit and transparent, resource-efficient, and does not require large training datasets or specialized hardware.

In the seminar we will discuss the method in detail, including how we define and create Generalized Events, which are the building blocks of GNs, and how we use knowledge graphs to generalize event components. We will also discuss how our method can predict missing events in a given narrative.

An evaluation of our method on a missing event prediction task reveals it as a strong competitor to a large language model baseline. This underscores that our method, while providing unique advantages like explicitness, transparency, and resource efficiency, distinct from deep learning, also holds its own in performance across many cases.