Film cinematography has been developed and applied for more than a century to involve and engage the viewer in visual storytelling. Interactive storytelling games can benefit from these cinematic conventions to enhance visual experience. However, even conversation scenes in games are highly dynamic, and pre-authoring camera parameters using cinematography principles is often insufficient. This paper proposes an automatic Visual Director System focused on dynamic conversation scenes involving three characters and reports on work in progress on a prototype applied to the recreation of a movie scene. Based on principles of cinematography and the study of film scenes, cinematic conventions for triadic conversations are encoded modularly as an artificial intelligence game component that selects suitable shots for dynamic scenes.

We demonstrate the Chimeria Platform that computationally models aspects of social identity dynamics for use in digital media such as in videogames and social networks. The Engine models users’ degrees of membership across multiple categories as gradient values, enabling more representational nuance than binary statuses of member/nonmember. The Application Interface handles user interaction and visuals for experiencing the narratives. Domain Epistemologies specify domain-specific ontologies that describe cultural knowledge and beliefs for each narrative. Our Visual Narrative Editor GUI is being developed to make authoring more accessible to a wider audience.

Prom Week is a social-simulation videogame driven by the artificial intelligence engine Comme il Faut (CiF). In each level of the game, the player selects social interactions between characters in an effort to achieve socially oriented goals. These social interactions are enacted with hand-authored natural-language dialogue exchanges, called instantiations, which also serve to render the underlying social considerations propelling the narrative at hand. While CiF's merit is in its capacity to richly model a social space, constraints rooted in authorial burden hinder Prom Week's ability to fully render CiF's rich social representations. What is needed is more instantiations, specifically instantiations that can render uncommon or complex game states with greater fidelity. We propose a technique to procedurally generate new, felicitous instantiations by recombination of dialogue segments from existing instantiations that are annotated, using the story-encoding tool Scheherazade, for their transmissions about the story world and their various dependencies.

AI research in interactive narrative often lacks specificity as to the player experience it is trying to enable. In this paper, we consider a set of desirable elements from narrative and interactive experiences, and show by looking at playable experiences from industry and academia that combining them has the potential to be limited or self-defeating. To address these issues, we propose opportunistic storytelling , a set of design principles for near-term playable interactive narratives.

We discuss a design pattern found in expressive storygames, the eurekon, which describes a specific dynamic arising from some adventure game puzzles where the player experiences a moment of revelation connecting the narrative and ludic planes. Eurekons have largely been designed out of modern storygames in favor of patterns that reduce the possibility of failure (as seen in the fall of the "puzzle" and rise of the "quest"), but this shift often eliminates the unique pleasures often found in a successful eurekon. We demonstrate both how the eurekon is a useful concept in analyzing existing adventure games and how it can inform designers hoping to create more successful eurekons.

This paper presents an interactive narrative framework we have designed for games that promote health behavior change. The framework aims to address two key issues: player engagement with the game, and player adherence to the health behavior change-related homework they receive in the game. In this paper, we describe our narrative system that tackles these issues and a prototype game that promotes physical activity in which our narrative system is integrated.

This article describes the process of designing a point-and-click adventure game that aimed at using dream logic as the basis to create its narrative puzzles. The technical solution to tackle this challenge was using Procedural Content Generation (PCG) as the design approach, which was used expressively to recreate the instability and changeability of dreams. Although PCG brought about replayability to the game, called Symon, it also created a series of other development problems, which had to be remedied through other design devices. One of the lessons learned during the development of the game is that PCG is not a blanket solution to problems, but rather an expressive tool to be used in combination to other design strategies; human factors are also key both during the development and reception of narrative video games.

As a story unfolds, it constructs a depiction of events, and at the same time, it also builds conceptual structure at a higher, interpretive level. This higher-level structure provides the terms for understanding the unfolding story, indicating what kinds of features and consequences characterize it – a story ontology . The process by which a tale constructs a story ontology is not straightforward, and in many ways is just as complex as the action at the event level. It involves an interaction between inferred situations and contexts, each with their own networks of terms and structures, which jostle for dominance. I refer to this interaction as governance . In this work, I demonstrate an example of governance at both levels, using a scene from the series Game of Thrones . When the interpretive terms of a story emerge, an understanding of what kinds of events might come next – the possible causal implications – are also conveyed, even if they are unexpected.

In this paper we address the task of extracting risk events and probabilities from free text, focusing in particular on the biomedical domain. While our initial motivation is to enable the determination of the parameters of a Bayesian belief network, our approach is not specific to that use case. We are the first to investigate this task as a sequence tagging problem where we label spans of text as events A or B that are then used to construct probability statements of the form P(A|B)=x. We show that our approach significantly outperforms an entity extraction baseline on a new annotated medical risk event corpus. We also explore semi-supervised methods that lead to modest improvement, encouraging further work in this direction.

In this paper, we focus on the problem of extracting structured labeled data from short unstructured ad-postings from online sources like Craigslist, where ads are posted on various topics, such as job postings, rentals, car sales etc. A fundamental challenge in addressing this problem is that most ad-postings are highly unstructured, short-text postings written in an informal manner with no inherent grammar or well-defined dictionary. In this paper, we propose unsupervised and supervised algorithms for extracting structured data from unstructured ads in the form of (key, value) pairs where the keys naturally represent topic-specific features in the ads. The unsupervised algorithm is centered around building an affinity graph, using the words from a topic-specific corpus of such ads where the edge weights represent affinities between words; the (key, value) extraction algorithm identifies specific groups of words in the affinity graph corresponding to different classes of key attributes. The supervised algorithm uses a Conditional Random Field based training algorithm to identify specific structured (key, value) pairs based on pre-defined topic-specific structural data representations of ads. Based on a corpus of car and apartment ad-postings from Craigslist, the unsupervised algorithm reported an accuracy of 67.74% and 68.74% for car and apartment ads respectively. The supervised algorithm demonstrated an improved performance with accuracies of 74.07% and 72.59% respectively.