Multi-Agent Narrative Experience Management as Story Graph Pruning

In interactive narratives, experience management is used to control the world and the NPCs a player interacts with, encouraging particular types of stories or discouraging others. The space of all stories in a narrative can be understood as a story graph, with story states as nodes and actions in the story as directed edges. In this paper, we present experience management as a graph pruning problem. Starting with the full story graph, edges representing NPC actions may be pruned until there is at most one action per NPC per state. With the full graph available, the choice of what to prune may consider all possible futures, and we can ensure that undesirable stories are not reachable. By never pruning player actions, we ensure the player may make any choice and still be accommodated in the story. When this method was used to manage the story of an adventure game, players found our technique generally produced higher agency and more-believable NPC behaviors than a control. Finally, we discuss scaling the results of this method for practical use.

Full Paper:



Stephen G. Ware, Edward T. Garcia, Mira Fisher, Alireza Shirvani, Rachelyn Farrell. Multi-agent narrative experience management as story graph pruning. IEEE Transactions on Games, 2022. (forthcoming)


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.

Full Article:



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.



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!