Narrative Planning for Belief and Intention Recognition

Planning algorithms generate sequences of actions that achieve a goal, but they can also be used in reverse: to infer the goals that led to a sequence of actions. Traditional plan-based goal recognition assumes agents are rational and the environment is fully observable. Recent narrative planning models represent agents as believable rather than perfectly rational, meaning their actions need to be justified by their goals, but they may act in ways that are not optimal, and they may possess incorrect beliefs about the environment. In this work we propose a technique for inferring the goals and beliefs of agents in this context, where rationality and omniscience are not assumed. We present two evaluations that investigate the effectiveness of this approach. The first uses partial observation sequences and shows how this impacts the algorithm’s accuracy. The second uses human data and compares the algorithm’s inferences to those made by humans.

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Rachelyn Farrell, Stephen G. Ware. Narrative planning for belief and intention recognition. In Proceedings of the 16th AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 52-58, 2020.

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Manipulating Narrative Salience in Interactive Stories using Indexter’s Pairwise Event Salience Hypothesis

The salience of a narrative event is defined as the ease with which an audience member can recall that past event. This article describes a series of experiments investigating the use of salience as a predictor of player behavior in interactive narrative scenarios. We utilize Indexter, a plan based model of narrative for reasoning about salience. Indexter defines a mapping of five event indices identified by cognitive science research onto narrative planning event structures. The indices—protagonist, time, space, causality, and intentionality—correspond to the “who, when, where, how, and why” of a narrative event, and represent dimensions by which events can be linked in short-term memory. We first evaluate Indexter’s claim that it can effectively model the salience of past events in a player’s mind. Next, we demonstrate that salience can be used to predict players’ choices for endings in an interactive story, and finally, we demonstrate that the same technique can be applied to influence players to choose certain endings.

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Rachelyn Farrell, Stephen G. Ware, and Lewis J. Baker. Manipulating Narrative Salience in Interactive Stories using Indexter’s Pairwise Event Salience Hypothesis. In IEEE Transaction on Games, vol. 12, num. 1, pp. 74-85, 2020.

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Playable:

Here are playable versions of each of the five Twine stories described in the article:

1. The prediction one (Section IV.2 in the article) – Here we successfully predicted people’s final choice based on the event indices of their earlier choices.

2. The first attempt at influence (Section IV.3.A, the no choices one) – We railroaded people through the beginning of the story, and expected them to choose the ending we targeted, based on what we had learned in the prediction study. But they didn’t!

3. The second attempt at influence (Section IV.3.B, the unrelated choices one) – Here we added some arbitrary choices throughout the story, in case the lack of interactivity was throwing off our game. But it still didn’t work!

4. The third attempt at influence (Section IV.3.C, the low agency choices one) – Realizing that it mattered which events people were given choices about, we engineered choices for the four key events used by our hypothesis that wouldn’t break our experimental design. I was really confident this was going to work, but once again, it did not!

5. The fourth attempt at influence (Section IV.3.D, the higher agency choices one) – Having finally really learned something, we made a slight adjustment to our experimental design that allowed us to give more “meaningful” choices for the key events, but still railroad them into our targeted ending… And IT WORKED!

Influencing 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—or prominence in memory—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 a past event is more salient if it shares one or more of these indices with the most recently narrated event. In a previous study we used this model to predict users’ choices in an interactive story based on the indices of prior events. We now show that we can use the same method to influence them to make certain choices. In this study, participants read an interactive story with two possible endings. We influenced them to choose a particular ending by manipulating the salience of story events. We showed that users significantly favored the targeted ending.

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Playable:

Play the story here.

Citation:

Rachelyn Farrell, Stephen G. Ware. Influencing user choices in interactive narratives using Indexter’s pairwise event salience hypothesis. In Proceedings of the 13th AAAI International Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 37-42, 2017.
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