Category: Research

A Possible Worlds Model of Belief for State-Space Narrative Planning

Abstract:

What characters believe, how they act based on those beliefs, and how their beliefs are updated is an essential element of many stories. State-space narrative planning algorithms treat their search spaces like a set of temporally possible worlds. We present an extension that models character beliefs as epistemically possible worlds and describe how such a space is generated. We also present the results of an experiment which demonstrates that the model meets the expectations of a human audience.

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Citation:

Alireza Shirvani, Stephen G. Ware, Rachelyn Farrell. A possible worlds model of belief for state-space narrative planning. In Proceedings of the 13th AAAI International Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 101-107, 2017.

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Prisoners’ Dilemma – Predicting Choices

This story was designed for the experiment in this paper. I was trying to predict people’s choices at the end based on what they do in the beginning. It worked pretty well!

I built it with the Twine interactive fiction engine. The beautiful art was done by Caroline Catlett Gates. The ugly art was me 😉

Click here to play.

Asking Hypothetical Questions about Stories using QUEST

Many computational models of narrative include representations of possible worlds—events that never actually occur in the story but that are planned or perceived by the story’s characters. Psychological tools such as QUEST are often used to validate computational models of narrative, but they only represent events which are explicitly narrated in the story. In this paper, we demonstrate that audiences can and do reason about other possible worlds when experiencing a narrative, and that the Quest knowledge structures for each possible world can be treated as a single data structure. Participants read a short text story and were asked hypothetical questions that prompted them to consider alternative endings. When asked about events that needed to change as a result of the hypothetical, they produced answers that were consistent with answers generated by QUEST from a different version of the story.  When asked about unrelated events, their answers matched those generated by QUEST from the version of the story they read.

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Citation:

Rachelyn Farrell, Scott Robertson, Stephen G. Ware. Asking hypothetical questions about stories using QUEST. In Proceedings of the 9th International Conference on Interactive Digital Storytelling, pp. 136-146, 2016.

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Predicting User Choices in Interactive Narratives using Indexter’s Pairwise Event Salience Hypothesis

Indexter is a plan-based model of narrative that incorporates cognitive scientific theories about the salience of narrative events. A pair of Indexter events can share up to five indices with one another: protagonist, time, space, causality, and intentionality. The pairwise event salience hypothesis states that when a past event shares one or more of these indices with the most recently narrated event, that past event is more salient, or easier to recall, than an event which shares none of them. In this study we demonstrate that we can predict user choices based on the salience of past events. Specifically, we investigate the hypothesis that when users are given a choice between two events in an interactive narrative, they are more likely to choose the one which makes the previous events in the story more salient according to this theory.

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Playable:

The story used for this study is here!

Citation:

Rachelyn Farrell, Stephen G. Ware. Predicting user choices in interactive narratives using Indexter’s pairwise event salience hypothesis. In Proceedings of the 9th International Conference on Interactive Digital Storytelling, pp. 147-155, 2016.

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Fast and Diverse Narrative Planning through Novelty Pruning

Novelty pruning is a simple enhancement that can be added to most planners. A node is removed unless it is possible to find a set of n literals which are true in the current state and have never all been true in any of that plan’s previous states. Expanding on the success of the Iterated Width algorithm in classical planning and general game playing, we apply this technique to narrative planning. Using a suite of 8 benchmark narrative planning problems, we demonstrate that novelty pruning can be used with breadth-first search to solve smaller problems optimally and combined with heuristic search to solve larger problems faster. We also demonstrate that when many solutions to the same problem are generated, novelty pruning can produce a wider variety of solutions in some domains.

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Citation:

Rachelyn Farrell, Stephen G. Ware. Fast and diverse narrative planning through novelty pruning. In Proceedings of the 12th AAAI International Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 37-43, 2016.

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