Eveil3d is a project for development of an immersive, virtual "serious game" for computer assisted foreign language learning, with which users interact verbally by means of an automatic speech recogniser. The speech of the target user group, namely adolescent low-proficiency non-native speakers, differs significantly from native adult speakers on which ASR systems are typically trained and thus on which they perform the best. As only a very small corpus of near-domain speech and text is available, the aforementioned difference becomes a development challenge. We deal with this challenge by adapting existing systems on the small data set. We adapt our language models using text selection to augment the in-domain data with similar data from out-of-domain sources. We adapt our acoustic models with MAP adaptation. Through these steps we achieve significant reductions in error.

Reasoning using expressive symbolic representations is a central theme of AI research, yet there are surprisingly few deployed games, even within the AIIDE research community, that use this sort of “classical” AI. This is partly due to practical and methodological issues, but also due to fundamental mismatches between current game genres and classical AI systems. I will argue that if we want to build games that leverage high-end classical AI techniques like commonsense reasoning and natural language processing, we will also have to develop new game genres and mechanics that better exploit those capabilities. I will also present a design sketch of a game that explores potential game mechanics for classical AI.

With more computing power available, video games may spare increasing amounts of processing time for AI. One prospective application of the newly available resources is the simulation of large amounts of non-player characters (NPCs) in open world games. While it is relatively easy to simulate simple behaviours of individual NPCs it is much more difficult to create meaningful interactions between the NPCs. However, without interaction, the world cannot look very alive. In this paper we present a technique that enriches the NPC simulation with pre-scripted situations - short sketches involving coordinated interaction between several NPCs that do not substantially alter the state of the game world but increase the appeal of the world to the player. We use constraint satisfaction techniques to find NPCs suitable to enact the situations at runtime. We have implemented situations on top of the AI system for an upcoming AAA open-world game and show that this approach satisfies functional and computational requirements for practical deployment in the final version of the game.

Current approaches to game design improvements rely on time-consuming gameplay testing processes, which rely on highly subjective feedback from a target audience. In this paper, we propose a generalizable approach for building predictive models of players’ emotional reactions across different games and game genres, as well as other forms of digital stimuli. Our input agnostic approach relies on the following steps: (a) collecting players' physiologically-inferred emotional states during actual gameplay sessions, (b) extrapolating the causal relations between changes in players' emotional states and recorded game events, and (c) building hierarchical cluster models of players' emotional reactions that can later be used to infer individual player models via fuzzy cluster membership vectors. We expect this work to benefit game designers by accelerating the affective play-testing process through the offline simulation of players' reactions to game design adaptations, as well as to contribute towards individually-tailored affective gaming.

In recent years, computer games have reached unprecedented level of graphical fidelity to the real world. As the non-player characters (NPCs) in the game world look more and more realistic, players expect them to manifest believable behavior as well. This is accented especially in games that feature large open worlds, which players may explore freely and it is thus not possible to explicitly account for all possible player interactions. In this paper we focus mainly on ambient AI - the logic behind day to day behaviors of NPCs as they sleep, work and entertain themselves in the virtual world. In this context, it is of great importance to build a system that handles many NPCs (up to several hundreds) quickly. In this paper we report on an implementation of a particular AI system that was approved for deployment in an upcoming high-budget game. The system features a hierarchy of control similar to the subsumption architecture and a visual agent-based language inspired by behavior trees. We describe the challenges involved in building such a system and specific design decisions we have made that let us achieve a level of behavioral fidelity unmatched by existing games. Finally we evaluate the performance of the system in a realistic setting.

The future of procedural content generation (PCG) lies beyond the dominant motivations of “replayability” and creating large environments for players to explore. This paper explores both the past and potential future for PCG, identifying five major lenses through which we can view PCG and its role in a game: data vs. process intensiveness, the interactive extent of the content, who has control over the generator, how many players interact with it, and the aesthetic purpose for PCG being used in the game. Using these lenses, the paper proposes several new research directions for PCG that require both deep technical research and innovative game design.

From an AI point of view, Real-Time Strategy (RTS) games are hard because they have enormous state spaces, they are real-time and partially observable. In this paper, we explore an approach to deploy game-tree search in RTS games by using game state abstraction, and explore the effect of using different abstractions over the game state. Different abstractions capture different parts of the game state, and result in different branching factors when used for game-tree search algorithms. We evaluate the different representations using Monte Carlo Tree Search in the context of StarCraft.

List of organizers of the Games and Natural Language Processing 2014 AIIDE Workshop.

We present a metaphor through which to study games: games as conversation, which casts gameplay as a communicative exchange between player and game. We propose to view aspects of gameplay as speech acts, as defined by Austin and Searle, and we present several examples that illustrate the diverse locutionary, illocutionary, and perlocutionary acts present in the design of digital games. Through our perspective, we are able to cast problems relevant to the interactive entertainment community as discourse problems, where an interactive system must determine what to “say,” in order to elicit in the minds of players a specific mental model that will allow them to perform successfully in the game. We conclude with a research agenda that proposes to leverage the artificial intelligence paradigm of discourse planning to tackle the discourse problems of interactive entertainment.

Dialogue authoring in large games requires not only content creation but the subtlety of its delivery, which can vary from character to character. Manually authoring this dialogue can be tedious, time-consuming, or even altogether infeasible. This paper utilizes a rich narrative representation for modeling dialogue and an expressive natural language generation engine for realizing it, and expands upon a translation tool that bridges the two. We add functionality to the translator to allow direct speech to be modeled by the narrative representation, whereas the original translator supports only narratives told by a third person narrator. We show that we can perform character substitution in dialogues. We implement and evaluate a potential application to dialogue implementation: generating dialogue for games with big, dynamic, or procedurally-generated open worlds. We present a pilot study on human perceptions of the personalities of characters using direct speech, assuming unknown personality types at the time of authoring.