Camelot: An Interactive Narrative Sandbox Tool

Camelot is a customizable engine for building simple 3D adventure games, designed for use in interactive narrative research. The engine is controlled by basic text commands sent over standard input, allowing a Camelot game to be controlled by an external program, or experience manager, that can be written in almost any programming language. You can find the latest release and API documentation using the above link.

The project’s design is a collaborative effort by all of us at the Narrative Intelligence Lab. It was built in Unity by Alex Shirvani and Edward Garcia. I wrote the in-house scripting language we use to quickly build Camelot experience managers for testing, demos, and experiments.

Camelot was first showcased as a Playable Experience at AIIDE 2018, and later as its own tutorial workshop at AIIDE 2019: CamJam.

Multi-Agent Narrative Experience Management as Story Graph Pruning

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.

Full Paper:

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Citation:

Stephen G. Ware, Edward T. Garcia, Alireza Shirvani, and Rachelyn Farrell. Multi-Agent Narrative Experience Management as Story Graph Pruning. In Proceedings of the 15th AAAI International Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 87-93, 2019.

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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.

Full Article:

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Citation:

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!

Combining Intentionality and Belief: Revisiting Believable Character Plans

In this paper we present two studies supporting a plan-based model of narrative generation that reasons about both intentionality and belief. First we compare the believability of character plans taken from the spaces of valid classical plans, intentional plans, and belief plans. We show that the plans that make the most sense to humans are those in the overlapping regions of the intentionality and belief spaces. Second, we validate the model’s approach to representing anticipation, where characters form plans that involve actions they expect other characters to take. Using a short interactive scenario we demonstrate that players not only find it believable when NPCs anticipate their actions, but sometimes actively anticipate the actions of NPCs in a way that is consistent with the model.

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Citation:

Alireza Shirvani, Rachelyn Farrell, Stephen G. Ware. Combining intentionality and belief: revisiting believable character plans. In Proceedings of the 14th AAAI International Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 222-228, 2018.

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Causal Link Semantics for Narrative Planning using Numeric Fluents

Narrative planners would be able to represent richer, more realistic story domains if they could use numeric variables for certain properties of objects, such as money, age, temperature, etc. Modern state-space narrative planners make use of causal links—structures that represent causal dependencies between actions—but there is no established model of a causal link that applies to actions with numeric preconditions and effects. In order to develop a semantic definition for causal links that handles numeric fluents and is consistent with the human understanding of causality, we designed and conducted a user study to highlight how humans perceive enablement when dealing with money. Based on our evaluation, we present a causal semantics for intentional planning with numeric fluents, as well as an algorithm for generating the set of causal links identified by our model from a narrative plan.

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Citation:

Rachelyn Farrell, Stephen G. Ware. Causal link semantics for narrative planning using numeric fluents. In Proceedings of the 13th AAAI International Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 193-199, 2017.

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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.

Full Paper:

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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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