This paper proposes the use of the Brownian distance correlation to conduct a lead-lag analysis of financial and economic time series. When this methodology is applied to asset prices, the non-linear relationships identified may improve the price discovery process of these assets. The Brownian distance correlation determines relationships similar to those identified by the linear Granger causality test, and it also uncovers additional non-linear relationships among the log prices of oil, coal, and natural gas.

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.

We present Tracery, an authoring tool for casual users to build grammars to generate interesting stories. While many modern story generation systems work to maintain narrative causality, generative systems like Racter show that non-causal and even nonsensical systems also have expressive power. Using design principles of direct manipulation and flow, Tracery is designed to allow users to author more stories with greater ease, and fully explore the affordances of grammar-based story generation. A small pilot test of users were able to quickly author engaging stories, and praised the system for its fun and usability.

In this paper, we translate a model of value-based emo- tional agents into an architecture for narrative characters and we validate it in a narrative scenario. The advantage of using such model is that different moral behaviors can be obtained as a consequence of the emotional ap- praisal of moral values, a desirable feature for digital storytelling techniques.

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.

We present a case-based approach to character identification in natural language text in the context of our Voz system. Voz first extracts entities from the text, and for each one of them, computes a feature-vector using both linguistic information and external knowledge. We propose a new similarity measure called Continuous Jaccard that exploits those feature-vectors to compute the similarity between a given entity and those in the case-base, and thus determine which entities are characters or not. We evaluate our approach by comparing it with different similarity measures and feature sets. Results show an identification accuracy of up to 93.49%, significantly higher than recent related work.