This paper describes the social physics game Slice of Life.In Slice of Life, the player strives to achieve various social goals by choosing social interactions for characters to engage in.These interactions are governed by a social simulation system called Ensemble with Social Practices (ESP).The ways to achieve the player's social goals are numerous and any given playthrough of the game will result in drastically different social worlds.Slice of Life also makes use of the underlying social simulation system's detailed state to generate symbolically grounded prompts for a large language model (LLM) that generates context-appropriate character dialogue.Rather than using LLMs for novelty or for economic reasons, the underlying social simulation technology, we argue, necessitates this approach in order to make it feasible to have nuanced dialogue that reflects the many ways characters could have gotten themselves into particular social situations.The purpose of this paper is to provide a detailed account of Slice of Life's design, how its social physics simulation enables interactive conversations based on social practices, and to illustrate how the generative possibilities of LLMs can be uniquely useful when applied as its natural language generation (NLG) system, without giving up authorial control of the gameplay or story.

The earliest years of life, from birth to elementary school, are the most critical time for children's social and emotional development. Recently, schools and workplaces have become increasingly concerned with cultivating social and emotional skills, especially with the decline of face-to-face interaction and the pervasive influence of modern communications technology. This paper proposes a game in development that navigates this challenge and aims to facilitate skill development in children ages 3-10 by having them identify, understand, feel their emotions, and regulate their actions. Using an established framework for social and emotional learning, it employs multi-agent artificial intelligence-based interactive gaming that generates dynamic scenarios and adjusts learning experiences based on individual child's or group's needs over time. We discuss the various modes of this game and its target frameworks, along with ways to evaluate effectiveness in facilitating social and emotional skill development.

This paper describes a prototype for the social physics game Slice of Life, and how it makes use of the underlying social simulation system’s sophisticated state to generate symbolically grounded prompts for a large language model (LLM) in order to generate context appropriate character dialogue. Rather than using LLMs for novelty or economic reasons, the underlying social simulation technology, we argue, necessitates this approach in order to make it feasible to have nuanced dialogue that reflects the many ways the characters could have gotten themselves into particular social situations. The primary goal of this paper is to illustrate how the generative possibilities of LLMs can be uniquely useful when applied as a controlled natural language generation (NLG) system, without giving up authorial control of the gameplay or story, or sacrificing accepted good game design practice.

This paper concerns developments in social physics rule and schema authoring made as part of the ongoing VESPACE project, a multinational research effort in developing a social physics driven virtual reality experience set in eighteenth-century France. Central to the theoretical application of social physics in this socio-historical context is the concept of citation as AI– that is, the historically-grounded reconstruction of human behavior represented by cited schemata, rules, and actions. In order to facilitate the authoring of domain-specific rules for the Ensemble social physics engine by history and cultural studies experts, we endeavored to develop a new collaborative authoring tool, which we tested in a series of workshops. The results of these workshops revealed various domain needs, as well as the shortcomings of representing data structures using one-to-one correspondences in UX/UI design.

Choice-driven narratives, such as those created through systems like Twine, are a compelling form of interactive storytelling that have been around for many years. But as long as this form has existed, it has grappled with a persistent design problem: consistently presenting choices that feel both effective and relevant. Brute force can achieve the desired effect, but usually at the cost of prohibitively high authorial burden. To tackle this, generative approaches, such as Mawhorter's Dunyazad, facilitate authoring procedural choice content for reuse and recombination. However, many such systems, while successful on technical levels, have yet to to be used to author large enough structures to support a full game, and require a high technical threshold for authors to use. To further development in this space, we present StoryAssembler, an open source generative narrative system that creates dynamic choice-driven narratives. It formed a critical part of Emma's Journey, an interactive narrative game, the initial version of which was collaboratively authored by a team of six writers. In the course of the game's creation, useful authoring patterns and design lessons were learned, as well as techniques that made the system approachable for first-time users.

Abstract—This paper presents Comme il Faut (CiF), an artifi-cial intelligence system thatmatches character performances to ap-propriate social context, with the goal of enabling authors to write high-level rules governing expected character behavior in given so-cial situations, rather than specific fixed choice points in a curated narrative structure. CiF models characters with a complex set of traits, feelings, and relationships, who can form intents, take ac-tions, relate to a shared cultural space, and remember and refer to past events. A set of authored rules encoding appropriate be-havior within a specific story world allow these characters to se-lect actions to take (and respond to actions by others) in a manner consistent with their own personal and social concerns as well as a shifting interpersonal context. Through the development and re-lease of PromWeek, a complete game using CiF as its narrative en-gine, we show how the system successfully creates complex narra-tives that are unique for each player and directed by those players’ attempts to make progress towards story goals. We also show how CiF continues to be used in several in-progress interactive experi-ences (Mismanor and IMMERSE), speaking to the utility and flex-ibility of its design. Index Terms—Artificial intelligence, emergent narrative, game design, interactive drama. I.

Prom Week places players in a typical high-school, abuzz with excitement over the upcoming prom. Players indirectly sculpt the social landscape by having these hapless highschoolers engage in social exchanges with each other. The results of these social exchanges are many and varied---ranging from mild fluctuations in respect to characters professing their eternal love for one another---and are informed by over 5,000 sociocultural considerations encoded in first order logic. Through massaging the interpersonal relationships and learning the personal intricacies of the characters, the player can solve a series of social puzzles; such as making the class-nerd the Prom King, or bringing peace between feuding jocks and preppies.

In this paper, we present Prom Week, a social simulation game about the interpersonal lives of a group of high school students in the week leading up to their prom. By starting the design of the game with a theory of social interaction, Prom Week is able to present satisfying stories that reflect the player's choices in a wide possibility space -- two features that rarely accompany one another. This paper reports the design details of how Prom Week utilizes social physics to achieve rich character specificity while maintaining a highly dynamic story space.

One compelling aspect of computer RPGs is the promise of player agency: the ability to make significant and desired choices in a large, complex, and story-rich environment. Giving players meaningful choice has traditionally required the creation of tremendous amounts of hand-authored story content. This authoring paradigm tends to introduce both structural and workload problems for RPG designers. Our hypothesis is that reducing authorial burden and increasing agency are two sides of the same coin, both requiring advancement in three distinct areas: (1) dynamic story management architecture that allows story elements to be selected and re-ordered in response to player choices; (2) dynamic dialogue generation which takes history and relationships into account; and (3) an authoring interface that lets writers focus on quests and characters. This paper describes SpyFeet, a playable prototype of a storytelling system designed to test this hypothesis.