This abstract describes the development of algorithms for personalized anxiety reduction feedback for use by a robot buddy interacting with a child about to receive intravenous therapy (an IV insertion). This three-phase study is currently being conducted; it consists of two data collections to determine domain-specific approaches, followed by the full study with personalized anxiety-reducing feedback. Participants receiving personalized feedback will be compared to participants with a non-personalized robot (to control for novelty) and a no robot condition (baseline control).

There has been much work on player modeling using game behavioral data collected. Many of the previous research projects that targeted this goal used aggregate game statistics as features to develop behavior models using both statistical and machine learning techniques. While existing methods have already led to interesting findings, we suspect that aggregated features discard valuable information such as temporal or sequential patterns, which may be important in deciphering information about decisionmaking, problem solving, or individual differences. Such sequential information is critical to analyze player behaviors especially in role-playing games (RPG) where players can face ample choices, experience different contexts, behave freely with individual propensities but possibly end up with similar aggregated statistics (e.g., levels, time spent). In this paper we intend to develop and apply a modeling technique that takes into consideration sequential patters to decipher individual differences in playing a Role Playing Game (RPG) game. Using an RPG with multiple affordances, we designed an experiment collecting granular in-game behaviors of 64 players. Using closed sequential pattern mining and logistic regression, we developed a model that uses gameplay action sequences to predict the real world characteristics, including gender, game play expertise and five personality traits (as defined by psychology). The results show that game expertise is a dominant factor that impacts in-game behaviors. The contribution of this paper is the algorithms we developed combined with a validation procedure to determine the reliability and validity of the results and the results themselves.

Cinematic, Ambient, Inhabitable Narrative Environments (CAINEs) are conceptual AI-driven interactive story systems combining text, audio, and visual imagery that are scalable and adaptable to a wide range of storytelling needs and interactor inputs. Conceived by at artist outside the AI community, they represent an opportunity to use AI in a nontraditional and immersive narrative fashion that relies not on the goal-based arrangement of story elements, but on the accretion and association of those elements in the minds of interactors. This paper represents the initial phase of the project’s development.

Narratives are powerful because of their impact on our emotional experiences. Recent years have witnessed significant advances in affective computing and intelligent interaction, presenting a broad range of opportunities for enhancing the design, implementation, and adaptivity of interactive narratives. This paper presents preliminary work examining story-centric games and interactive narratives from the perspective of psychological theories of emotion, with a particular focus on player affect. We examine the sources and duration of player emotion, social facets of emotion, players’ individual differences in emotion, and meta-emotions. Recommendations and future directions for research on player emotion in interactive narratives are discussed.

Research has indicated that role models have the potential to boost academic performance. In this paper, we describe an experiment exploring role models as game avatars in an educational game. Of particular interest are the effects of these avatars on players' performance and engagement. Participants were randomly assigned to a condition: a) user selected role model avatar, or b) user selected shape avatar. Results suggest that role models are heavily preferred. African American participants had higher game affect in the role model condition. South Asian participants had higher self-reported engagement in the role model condition. Participants that completed <= 1 levels had higher performance in the role model condition. General trends suggest that the role model's gender and racial closeness with the player, could play a role in player performance and self-reported engagement as consistent with the social science literature.

We sketch the process of creating a novel video game by blending two video games specified in the Video Game Description Language (VGDL), following the COINVENT computational model of conceptual blending. We highlight the choices that need to be made in this process, and discuss the prospects for a computational game designer based on blending.

GHOST is a framework to help game developers to model and implement their own optimization problems, or to simply instantiate a problem already encoded in GHOST. Previous works show that GHOST leads to high-quality solutions in some tens of milliseconds for three RTS-related problems: build order, wall-in placement  and target selection. In this paper, we show the robustness of the framework, having very good results for a problem it is not designed for (pathfinding), as well as its flexibility, where it is easy to propose different models of the same problem (resource allocation problem). The goal of the paper is not to improve the state-of-the-art on these problems, but to use them as benchmarks to test GHOST properties.

Context personalisation is a flourishing area of research with many applications. Context personalisation systems usually employ a user model to predict the appeal of the context to a particular user given a history of interactions. Most of the models used are context-dependent and their applicability is usually limited to the system and the data used for model construction. Establishing models of user experience that are highly scalable while maintaing the performance constitutes an important research direction. In this paper, we propose generic models of user experience in the computer games domain. We employ two datasets collected from players interactions with two games from different genres where accurate models of players experience were previously built. We take the approach one step further by investigating the modelling mechanism ability to generalise over the two datasets. We further examine whether generic features of player behaviour can be defined and used to boost the modelling performance. The accuracies obtained in both experiments indicate a promise for the proposed approach and suggest that game-independent player experience models can be built.

Hand-coded finite-state machines and behavior trees are the go-to techniques for artificial intelligence (AI) developers that want full control over their character's bearing. However, manually crafting behaviors for computer-controlled agents is a tedious and parameter-dependent task. From a high-level view, the process of designing agent AI by hand usually starts with the determination of a suitable set of action sequences. Once the AI developer has identified these sequences he merges them into a complete behavior by specifying appropriate transitions between them. Automated techniques, such as learning, tree search and planning, are on the other end of the AI toolset's spectrum. They do not require the manual definition of action sequences and adapt to parameter changes automatically. Yet AI developers are reluctant to incorporate them in games because of their performance footprint and lack of immediate designer control. We propose a method that, given the symbolic definition of a problem domain, can automatically extract a transparent behavior model from Goal-Oriented Action Planning (GOAP). The method first observes the behavior exhibited by GOAP in a Monte-Carlo simulation and then evolves a suitable behavior tree using a genetic algorithm. The generated behavior trees are comprehensible, refinable and as performant as hand-crafted ones.

Avatar customization systems enable players to represent themselves virtually in many ways. Research has shown that players exhibit different preferences and motivations in how they customize their avatars. In this paper, we present a data-driven analytical approach to modeling player behavioral patterns exhibited during the avatar customization process. We used our data mining tool \textit{AIRvatar} to analyze telemetry data obtained from 190 players using an avatar creator of our own design. Using non-negative matrix factorization (NMF) and N-gram models, we demonstrate how our approach computationally models behavioral patterns exhibited by players such as "regular shopping," "engaged shopping," or "bored browsing". Our models obtained significant effect sizes (0.12 <= R^2 <= 0.54) when validated with multiple linear regressions for players' time spent engaging in activities within the avatar creator. The NMF model had comparably high performance and ease of interpretation compared to control models.