Stephen G. Ware

A Computational Model of Plan-Based Narrative Conflict at the Fabula Level

Conflict is an essential element of interesting stories. In this paper, we operationalize a narratological definition of conflict and extend established narrative planning techniques to incorporate this definition. The conflict partial order causal link planning algorithm (CPOCL) allows narrative conflict to arise in a plan while maintaining causal soundness and character believability. We also define seven dimensions of conflict in terms of this algorithms knowledge representation. The first three-participants, reason, and duration-are discrete values which answer the “who?” “why?” and “when?” questions, respectively. The last four-balance, directness, stakes, and resolution-are continuous values which describe important narrative properties that can be used to select conflicts based on the authors purpose. We also present the results of two empirical studies which validate our operationalizations of these narrative phenomena. Finally, we demonstrate the different kinds of stories which CPOCL can produce based on constraints on the seven dimensions.

Four quantitative metrics describing narrative conflict

Conflict is an essential element of interesting stories. In previous work, we proposed a formal model of narrative conflict along with 4 quantitative dimensions which can be used to distinguish one conflict from another based on context: balance, directness, intensity, and resolution. This paper presents the results of an experiment designed to measure how well these metrics predict the responses of human readers when asked to measure these same values in a set of four stories. We conclude that our metrics are able to rank stories similarly to human readers for each of these four dimensions.

A computational model of narrative conflict

Conflict is a key feature of interesting stories. Building on previous narrative systems, I intend to formalize a computational model of conflict to inform the creation of plots which more closely match human story expectations. I have proposed a means of generating stories based on AI planning and have identified seven important dimensions of conflict: participants, subject, duration, directness, intensity, balance, and resolution. At this consortium, I hope to receive feedback on the model, along with suggestions for its use in an empirical evaluation.

A Survey and Analysis of Techniques for Player Behavior Prediction in Massively Multiplayer Online Role-Playing Games

While there has been much research done on player modeling in single-player games, player modeling in massively multiplayer online role-playing games (MMORPGs) has remained relatively unstudied. In this paper, we survey and evaluate three classes of player modeling techniques: 1) manual tagging; 2) collaborative filtering; and 3) goal recognition. We discuss the strengths and weaknesses that each technique provides in the MMORPG environment using desiderata that outline the traits an algorithm should posses in an MMORPG. We hope that this discussion as well as the desiderata help future research done in this area. We also discuss how each of these classes of techniques could be applied to the MMORPG genre. In order to demonstrate the value of our analysis, we present a case study from our own work that uses a model-based collaborative filtering algorithm to predict achievements in World of Warcraft. We analyze our results in light of the particular challenges faced by MMORPGs and show how our desiderata can be used to evaluate our technique.

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.

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 QKSs 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.

Intentionality and Conflict in The Best Laid Plans Interactive Narrative Virtual Environment

In this paper, we present The Best Laid Plans, an interactive narrative adventure game, and the planning technologies used to generate and adapt its story in real time. The game leverages computational models of intentionality and conflict when controlling the non-player characters (NPCs) to ensure they act believably and introduce challenge into the automatically generated narratives. We evaluate the games ability to generate NPC behaviors that human players recognize as intentional and as conflicting with their plans. We demonstrate that players recognize these phenomena significantly more than in a control with no NPC actions and not significantly different from a control in which NPC actions are defined by a human author.

Reports on the 2012 AIIDE Workshops

The 2012 AIIDE Conference included four workshops: Artificial Intelligence in Adversarial Real-Time Games, Human Computation in Deigital Entertainment and AI for Serious Games, Intelligent Narrative Technologies, and Musican Metacreation. The workshops took place October 8-9, 2012 at Stanford University. This report contains summaries of the activities of those four workshops.