script learning
**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.