This demo paper presents an interactive narrative game where players intervene in Shakespearean scenes to deescalate imminent violence using natural speech. The system employs a hybrid architecture: modern natural language processing (NLP) tools (speech-to-text, large language models, sentence transformers) interpret user utterances for known de-escalation strategies (e.g., active listening, redirection) and gauge their effectiveness, while symbolic systems model character emotional stances, beliefs, and personality traits that affect input interpretation, in order to select fitting responses. The project contributes an investigation of current NLP capabilities for interactive character-driven games and serves a pedagogical purpose, teaching conflict resolution and violence prevention within a broader educational framework.

Common approaches to behavioral artificial intelligence, like behavior trees, utility-based approaches, and machine learning are often lacking with regards to expressivity of the decision-making process. Behavior trees are too rigid; utility-based approaches are focused on outcomes, not process; and machine learned agents seek only to maximize reward and minimize loss. A combination of AI approaches, however, may provide more nuanced, visible, and coherent processes that can be realized through the performance of character behaviors. In this extended abstract, we present Viv: a system that blends defeasible logic programming with dynamic behavior trees. To our knowledge, no extant works have bridged the gap between defeasible reasoning and action, particularly in regards to the performances of virtual nonplayer characters. We begin by explaining the design of our system, which was heavily influenced by the Stanislavski method of acting. We then describe the technical implementation of our system. Finally, we describe future directions of research that could provide benefit to, or benefit from, our system.

Many genres of video games value the fidelity of background non-player characters (NPCs) for they create the illusion of life necessary to support a believable world. However, in some genres of games, there is often a layer of abstraction between player actions and how NPCs process and respond to those actions. In this work, we argue that a system of computational gossip, grounded in sociological and social-psychological theory, can reduce that abstraction and improve believability of NPCs by deepening an NPCs reasoning of player action. In the form of a case study, this work presents the game Sunset Valley: an early step towards a system of computational gossip. Then by inspecting Sunset Valley’s gossip model, we analyze our game by exploring several well-established sociological and social-psychological frameworks, identify which frameworks are well-represented in Sunset Valley, and which frameworks are not. Finally, we conclude to what extant artificial intelligence systems can be used to address the shortcomings of our system.

Game mechanics, the affordances they create for players, and the agency experienced by players as a result of interacting with those mechanics are all, indisputably, intertwined. This work seeks to explore the relationship between game mechanics and agency by way of building an agency-centered ontology of video game mechanics. Building on existing framings of agency, we devise a set of agency-centered domains, properties belonging to those domains, and questions about game mechanics that reveal those properties. We build a dataset of game mechanics from different games and, using our domain-specific set of questions, illuminate properties of those game mechanics. Finally, we explore how those properties are interrelated, and identify examples of game mechanics that, according to our analysis, inspire the most agency.