An Introduction to AI Story Generation

This is an article from Popular Mechanics in 1931.

1. What is Automated Story Generation?

  • Narrative: The recounting of a sequence of events that have a continuant subject and constitute a whole (Prince, 1987). An event describes some change in the state of the world. A “continuant subject” means there is some relationship between the events—it is about something and not a random list of unrelated events. What “relates” events is not entirely clear but I’ll get to that later.
  • Story: A narrative that tells a story has certain properties that one comes to expect. All stories are narratives, but not all narratives are stories. Unfortunately I cannot point to a specific set of criteria that makes people regard a narrative as a story. One strong contender, however, is a structuring of events in order to have a particular effect on an audience.
  • Plot: A plot is the outline of main incidents in a narrative.

2. Why Study Automated Story Generation?

  • Human-AI coordination: there are times when it is easier to communicate via narrative. For example, communicating via vignettes helps with coordination because it sets expectations against which to guage the appropriateness of behavior. Humans often find it easier to explain via vignettes, and are often able to more easily process complex procecural information via vignettes.
  • Human-AI rapport: Telling and listening to stories is also a way that humans build rapport.
  • Explainable AI: Explanations can help humans understand what an AI system does. For sequential decision making tasks (e.g. robotics) this might entail a temporal component to the explanation resembling a story.
  • Computer games: many computer games feature stories or plots, which can be generated or customized. Going beyond linear plots, interactive stories are those in which the user assumes the role of a character in a story and is able to change the story with their actions. To be able to respond to novel user actions requires the ability to adapt or re-write the plot.
  • Training and education: inquiry-based learning puts learners in the role of experts and scenarios can be generated to meet pedagogical needs (similar to interactive stories above).

3. Narratology and Narrative Psychology

  • Fabula: The fabula of a narrative is an enumeration of all the events that occur in the story world between the time the story begins and the time the story ends. The events in the fabula are temporally sequenced in the order that they occur, which is not necessarily the same order in which they are told. Most notably, the events in the fabula might not all exist in the final telling of the narrative; some events might need to be inferred from what is actually told. For example: “John departs his house. Three hours later John arrives at the White House. John mutters about the traffic jam.” The fabula clearly contains the events “John departs house” and “John arrives at the White House” and “John mutters”. We might infer that John also drove a car and was stuck in a traffic jam — an event that was not explicitly mentioned and furthermore would have happened between “depart” and “arrive” instead of afterward when the first clue is given.
  • Sjuzhet: The sjuzet of a narrative is a subset of the fabula that is presented via narration to the audience. It is not required to be told in chronological order, allowing for achronological tellings such as flash forward, flashback, ellipses (gaps in time), interleaving, achrony (randomization), etc.

4. Non-Learning Story Generation Approaches

4.1. Story Grammars

The earliest known story generated by a grammar-based story generation system (1960).
The Rumelhart story grammar.

4.2. Story Planners

A story generated by the Tale Spin system.
Mis-spun tales generated by the Tale Spin system.
A plot fragment schema from the Universe system.
A story generated by the Universe system.
An action schema for a POCL planner.
A story plan generated by a POCL planner from Riedl and Young (2010).
A story plan generated by Fabulist. The orange bubbles show actions that are part of goal hierarchies.
A story generated by Fabulist corresponding to the above plan data structure.
An example CPOCL plan with character conflict and un-executed actions.
A story generated by CPOCL.

4.3. Case Based Reasoning

A story generated by the Minstrel system.
Case library for the ProtoPropp system.
A story generated by the Mexica system. Regular text was generated during the engagement phase. Text in italics was generated during the reflection phase.

4.4. Character-Based Simulation

The hierarchical task network for character agents in a story simulation.

5. Machine Learning Story Generation Approaches

A plot graph learned by Scheherazade for going on a date to a movie theatre.
A story generatedy by Scheherazade for the plot graph above.

6. Neural Story Generation Approaches

6.1. Neural Language Models

The generation loop for Martin et al. (2018).

6.2. Controllable Neural Story Generation

Fine-tuning the event2event neural network from the Martin et al. (2018) framework.
Fine-tuning reward is calculated by analyzing how close verbs are to each other in the corpus.
Stories generated by the hierarchical fusion model.
Stories generated by the plan-and-write system.
Illustration of inputs and outputs of the PlotMachines system.

6.3. Neuro-Symbolic Generation

The neurosymbolic architecture by Martin. The World Engine maintains a set of propositions about the story world.
The CAST pipeline.

6.4. Other Neural Approaches

The graph built by C2PO. 1) the initial event. 2) the final event. 3) the event found that bridges the forward and backward sub-graphs.
Example stories generated by C2PO.
Stories generated via question-answering. The bold text is given input.

7. Conclusions

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AI for storytelling, games, explainability, safety, ethics. Assoc. Professor @GeorgiaTech . Associate Director @MLatGT . Time travel expert. Geek. Dad. he/him

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Mark Riedl

Mark Riedl

AI for storytelling, games, explainability, safety, ethics. Assoc. Professor @GeorgiaTech . Associate Director @MLatGT . Time travel expert. Geek. Dad. he/him

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