We address the challenges of evaluating the fidelity of AI agents that are attempting to produce human-like behaviors in games. To create a believable and engaging game play experience, designers must ensure that their non-player characters (NPCs) behave in a human-like manner. Today, with the wide popularity of massively-multi-player online games, this goal may seem less important. However, if we can reliably produce human-like NPCs, this can open up an entirely new genre of game play. In this paper, we focus on emulating human behaviors in strategic game settings, and focus on a Social Ultimatum Game as the testbed for developing and evaluating a set of metrics for comparing various autonomous agents to human behavior collected from live experiments.

Current behavior authoring tools force game designers to split their attention between the game context and the tool context. We have addressed this problem by developing a behavior authoring tool that merges these two contexts. This paper outlines the design and implementation of a gameindependent behavior tree authoring tool, called AIPaint, that allows a designer to create and edit behavior trees via a natural sketching interface overlaid on the game world. We demonstrate the use of AIPaint to author computercontrolled characters in two simple games and report on an observational evaluation.

Playtesting is the primary process that allows a game designer to access game quality. Current playtesting methods are often intrusive to play, involves much manual labor, and might not even portray the player's true feedback. This paper aims to alleviate these shortcomings by presenting the position that state of the art artificial intelligence techniques can construct automated playtesting systems that supplement or even substitute this process to a certain extent. Several potential research directions are proposed in this theme. A work-in-progress report is also included to demonstrate the conceptual feasibility of the potentials of this research area.

We propose the creation of computational caricatures as a design research practice that aims to advance understanding of the game design process and to develop the reusable technology for design automation. Computational caricatures capture and exaggerate statements about the game design process in the form of computational systems (i.e. software and hardware). In comparison with empirical interviews of game designers, arguments from established design theory, and the creation of neutral simulations of the design process, computational caricatures provide more direct access to inquiry and insight about design. Further, they tangibly demonstrate architectures and subsystems for a new generation of human-assisting design support systems and adaptive games that embed aspects of automated design in their runtime processes. In this paper, we frame the idea of computational caricature, review several existing design automation prototypes through the lens of caricature, and call for more design research to be done following this practice.

Game metrics are an approach to understanding games and gameplay by analyzing and visualizing information collected from players in playtests. This paper proposes that another source of metrics is the game itself, and that not all information needs to (or ought to) come from empirical playtests. I discuss seven strategies for extracting information from games, and discuss how the information retrieved in this manner relates to empirical playtest metrics---which it differs from but can often complement.

Due to the changing nature of videogames, the component-based architecture is the design of choice for managing game entities instead of the traditional static class hierarchies. A component-based architecture lets programmers edit entities as collections of components, which provide the entity with new functionalities. Such architecture promotes flexibility but makes the code more difficult to understand because entities are built at runtime by linking components. In this paper we present a semi-automatic process for moving from a class hierarchy to a component-based architecture. Through the application of Formal Concept Analysis we propose a novel technique for automatically identifying candidate distributions of responsibilities among components.

This paper presents the latest version of the Machinations framework. This framework uses diagrams to represent the flow of tangible and abstract resources through a game. This flow represents the mechanics that make up a game’s interbal economy and has a large impact on the emergent gameplay of most simulation games, strategy games and board games. This paper shows how Machinations diagrams can be used simulate and balance games before they are built.

A growing body of work in games research, both generative and analytic, seeks to characterize the relationship between a player’s understanding of an interactive narrative and her options for action within it. This paper provides several definitions that collectively serve as a basis for a model of the user’s comprehension of an unfolding story in a game. Central to this approach, we define the notion of narrative affordance. In essence, a game provides a narrative affordance for some course of action when a player can imagine that course of action as part of a story that completes their current story experience. To define narrative affordance, we draw links from cognitive models of narrative comprehension and a range of research on affordance, which we couple with planning approaches to story and discourse generation. In our approach, we view the creation of an interactive narrative that provides a high degree of agency as a discourse generation problem. We posit that an interactive narrative system must reason about the content and organization of its communication with a player in order to prompt a player’s understanding about the game’s story and her role in it. This paper ends by pointing toward a research direction intended to provide insight into a range of aspects of interactive narrative, including role, genre, choice and agency.

Interactive Narrative often involves dialogue with virtual dramatic characters. In this paper we compare two kinds of models of character style: one based on models derived from the Big Five theory personality, and the other derived from a corpus-based method applied to characters and films from the IMSDb archive. We apply these models to character utterances for a pilot narrative-based outdoor augmented reality game called Murder in the Arboretum . We use an objective quantitative metric to estimate the quality of a character model, with the aim of predicting model quality without perceptual experiments. We show that corpus-based character models derived from individual characters are often more detailed and specific than personality based models, but that there is a strong correlation between personality judgments of original character dialogue and personality judgments of utterances generated for Murder in the Arboretum that use the derived character models.

This paper presents a framework that facilitates communication between a planning system (“planner”) and a plan execution system (“executor”) to enable them to run concurrently, with the main emphasis on meeting the real-time requirements of the application domain. While the framework is applicable to general-purpose planning, its features are optimized for the requirements of automated story planning for games—with emphasis on monitoring player-triggered events and handling on-time (re-)generation of story assets such as characters, maps and scenarios. This framework subsumes the traditional interleaved planning-and-execution paradigm used in embedded continual planning systems and generalizes it to a non-embedded context, making the framework ideal for use with contemporary game architectures (e.g., multithreaded game engines, or games with subsystems communicating over a network).