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 — 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.

Section: Introductionmentioning

confidence: 88%

Section: Pairwise Event Saliencementioning

confidence: 97%

Influencing User Choices in Interactive Narratives Using Indexter’s Pairwise Event Salience Hypothesis

Farrell

Ware

2021

“…Some systems require story graphs to be acyclic, implying some constraints on history, but we do not require acyclic graphs. The Markov assumption is a limitation for story graph systems, because different actions leading to the same state can suggest very different future actions (Farrell and Ware 2016). Given that our graphs are hundreds of millions of nodes and already straining the limits of what can be feasibly computed, we accept this simplifying assumption for our initial work.…”

Section: Related Work Story-graph-based Systemsmentioning

confidence: 99%

Multi-Agent Narrative Experience Management as Story Graph Pruning

Ware

Garcia

Shirvani

et al. 2019

In many intelligent interactive narratives, the player controls an avatar while an experience manager controls non-player characters (NPCs). The space of all stories can be viewed as a story graph, where nodes are states and edges are actions taken by the player, by NPCs, or by both jointly. In this paper, we cast experience management as a story graph pruning problem. We start with the full graph and prune intelligently until each NPC has at most one action in every state. Considering the entire graph allows us to foresee the long-term consequences of every pruning decision on the space of possible stories. By never pruning player actions, we ensure the experience manager can accommodate any choice. When used to control the story of an adventure game, players found our technique generally produced higher agency and more believable NPC behavior than a control.

“…A particular challenge that I want to address, however, is the non-Markovian nature of stories. Different sequences of actions that lead to the same "state" may suggest very different paths for the future (Farrell and Ware 2016). A narrative representation that ignores history is therefore bound to suffer some limitations.…”

Section: Introductionmentioning

confidence: 99%

Experience Management with Beliefs, Desires, and Intentions for Virtual Agents

Farrell

2018

Intelligent interactive narrative systems often use an experience manager to govern the behavior of non-player charactersin a way that guides the story towards its author’s agenda, which may be for entertainment, education, training, or other purposes. For such systems, a central challenge is creating believable virtual characters. The Belief Desire Intention framework is often cited as a goal for researchers in this field; for characters to seem realistic, a human audience should attribute beliefs, desires, and intentions to them. Much of my prior work has focused on belief; my goal for the future is to finish the work on belief, and to implement a new model of desire and intention that explicitly reasons about characters’ commitment to certain plans of action.