In this paper we focus on proactive behavior for non-player characters (NPCs) in the first-person shooter (FPS) genre of video games based on goal-oriented planning. Some recent approaches for applying real-time planning in commercial video games show that the existing hardware is starting to follow up on the computing resources needed for such techniques to work well. Nonetheless, it is not clear under which conditions real-time efficiency can be guaranteed. In this paper we give a precise specification of SimpleFPS, a STRIPS planning domain expressed in PDDL that captures some basic planning tasks that may be useful in a first person shooter video game. This is intended to work as a first step towards quantifying the performance of different planning techniques that may be used in real-time to guide the behavior of NPCs. We present a simple tool we developed for generating random planning problem instances in PDDL with user defined properties, and show some preliminary results based on SimpleFPS instances that vary in the size of the domain and two well-known planners from the planning community.

In this paper, we discuss approaches to dialogue in interactive video games and interactive narrative research. We propose that situating interactive dialogue in the simplified expectations of video games is a profitable way to investigate computational dialogue simulation. Taking cues from existing physical simulations such as combat, we propose a hypothetical game environment and design goals for an embedded interactive dialogue system. We present a modular framework targeted at that environment, which is designed to enable incremental development and exploration of dialogue concepts. We describe this framework together with a work-in-progress system for simulating simple in-game negotiation dialogues.

We present ongoing work on a system that accommodates player agency in a digital narrative with an external plot. We focus on key events that should occur in that storyline for dramatic effect, but do not explicitly specify the characters that should fill the roles needed for those events. Instead, we define them abstractly, with characteristics that the selected characters should have (including previous events they should have completed for eligibility), and rely on a Director construct to populate those roles from agents in the selection pool that fit those criteria. Agents begin as largely homogeneous, primordial entities that accumulate data and narrative value from the events in which they participate. This creates an environment that differentiates characters by the actions they perform, conferring worth onto characters that become important to the player based on their direct involvement in the plot. The focus, then, is on defining a priori the what of the narrative, while leaving it to the Director construct to decide at runtime exactly who among a distributed pool of agents carries it out.

Characters are a critical part of storytelling and emotion is a vital part of character. Readers generally credit characters with human emotions, and it is these emotions which bring meaning to stories. To computationally construct interesting and meaningful stories we need a model of emotion which allows us to predict characters’ reactions to events in the world. There are many different psychological theories of emotion; the most popular to date for computational applications is the OCC theory. This paper describes a Discrete Event Calculus implementation of the OCC Theory of Emotion. To evaluate our system, we apply it to a selection of Aesop’s fables, and compare the output to the emotions readers expect in the same situations based on a survey.

Drama Managers, a specific type of the more general Experience Manager, have become a common subject of study in the interactive narrative literature. With a range of representational and computational approaches, authors have repeatedly developed techniques that enable computers to generate, reason about, and adapt narratives in an interactive virtual setting. In order to fully realize an experience manager, seven representational and computational problems need to be solved, generally on a case-by-case basis. In other words, the choice to use an Experience Manager is the choice to model the design as, and implement solutions to, seven inter-dependent design problems. We explicitly articulate those design problems and provide a number of examples of methods that both motivate the design problems as well as illustrate a range of approaches to solving them.

Interactive narrative environments offer significant potential for creating engaging narrative experiences that are tailored to individual users. Increasingly, applications in education, training, and entertainment are leveraging narrative to create rich interactive experiences in virtual storyworlds. A key challenge posed by these environments is devising accurate models of director agents’ strategies that determine the most appropriate director action to perform for crafting customized story experiences. A promising approach is developing an empirically informed model of director agents’ decision-making strategies. In this paper, we propose a framework for learning models of director agent decision-making strategies by observing human-human interactions in an interactive narrative-centered learning environment. The results are encouraging and suggest that creating empirically driven models of director agent decision-making is a promising approach to interactive narrative.

Much of the most expressive behavior in humans - expressions of shock or alarm, gaze aversion, or explosive rage - are the result of automatic processes that engage before deliberative processing can respond. In some cases, such as weeping, the deliberative system may have only limited ability to override the automatic system. These processes are implemented by a network of phylogenetically old, special purpose, somewhat redundant systems that give rise to the particular idiosyncratic behavior we associate with automatic reactions to emotional events. In this paper, I'll review some of the ethological and neuropsychological results on low-level systems related to threat response, and their relation to the simulation of virtual characters. I will also discuss work in progress on building a medium-fidelity simulation of these systems.

We present a system that uses evolutionary optimization to suggest new story-world events that, if added to an existing interactive story, would most improve the average interactive experience, according to author-supplied criteria. In doing so, we aim to apply some of the ideas from drama-managed storytelling, such as authorial aesthetic control, in an unguided setting more akin to emergent storytelling: rather than guiding or directing a player towards an experience in line with an author's aesthetic goals, the storyworld is augmented with new content in a way that will tend to align with an author's goals, even if the player is not guided. In this paper, we present an offline system, and demonstrate its robustness to a number of variations in authorial criteria and player-model assumptions. This is intended to lay the groundwork for a future system that would generate new content online, allowing for interactive stories larger than those explicitly written by the author.

Text annotations are of great use to researchers in the language sciences, and much effort has been invested in creating annotated corpora for an wide variety of purposes. Unfortunately, software support for these corpora tends to be quite limited: it is usually ad-hoc, poorly designed and documented, or not released for public use. I describe an annotation tool, the Story Workbench, which provides a generic platform for text annotation. It is free, open-source, cross-platform, and user friendly. It provides a number of common text annotation operations, including representations (e.g., tokens, sentences, parts of speech), functions (e.g., generation of initial annotations by algorithm, checking annotation validity by rule, fully manual manipulation of annotations) and tools (e.g., distributing texts to annotators via version control, merging doubly-annotated texts into a single file). The tool is extensible at many different levels, admitting new representations, algorithm, and tools. I enumerate ten important features and illustrate how they support the annotation process at three levels: (1) annotation of individual texts by a single annotator, (2) double-annotation of texts by two annotators and an adjudicator, and (3) annotation scheme development. The Story Workbench is scheduled for public release in March 2012.

Annotated corpora have stimulated great advances in the language sciences. The time is ripe to bring that same stimulation, and consequent benefits, to computational approaches to narrative. I describe an effort to construct a corpus of semantically annotated stories. I outline the structure of the corpus, a structure which colloquially can be described as a "handful of handfuls." One handful of the corpus has already been constructed, viz., 18k words of Russian folktales. There are two handfuls under construction: legal cases focused on the area of probable cause, and stories from Islamist Extremist Jihadists. Four more handfuls are being planned: folktales from Chinese, English, and a West Asian culture, and stories of international conventional and cyber conflicts. There are numerous additional handfuls under discussion. The main focus of the corpus so far has been on textual materials that are annotated for their surface semantics using conventional annotation tools and techniques; nonetheless, there are numerous novel dimensions along which the corpus might grow and become useful for different communities. In particular I propose for discussion the outlines of a few novel sources, annotation schemes, and collection methodologies that could potentially make the corpus of great use to the interactive narrative or narrative generation communities.