Script learning uses AI to learn typical event sequences — discovering common patterns like "restaurant script" (enter → order → eat → pay → leave) or "job interview script," enabling prediction of what typically happens next and understanding of routine activities.

What Is Script Learning?

• Definition: Learn stereotypical event sequences from data.
• Scripts: Knowledge structures for routine activities.
• Example: Restaurant script, airport script, shopping script.
• Goal: Understand typical event sequences and predict next events.

Script Components

Events: Typical actions in sequence (order food, eat, pay bill). Participants: Typical roles (customer, waiter, chef). Props: Objects involved (menu, food, money). Preconditions: What must be true before script. Effects: What changes after script. Variations: Alternative paths through script.

Why Script Learning?

• Commonsense Reasoning: Understand routine activities.
• Event Prediction: Predict what typically happens next.
• Narrative Understanding: Fill in implicit events.
• Anomaly Detection: Identify unusual event sequences.
• Planning: Generate plans for achieving goals.

Learning Approaches

Unsupervised: Discover scripts from large text corpora. Clustering: Group similar event sequences. Probabilistic Models: Learn event transition probabilities. Neural Models: RNNs, transformers learn event sequences. Knowledge Extraction: Mine scripts from how-to articles, narratives.

Applications: Narrative understanding, story generation, commonsense reasoning, activity recognition, plan generation, chatbots.

Challenges: Script variations, cultural differences, implicit events, rare scripts, script boundaries.

Datasets: InScript, DeScript for script learning research.

Tools: Research systems for script induction, event sequence models.