Christoffer Holmgård

Procedural Personas as Critics for Dungeon Generation

This paper introduces a constrained optimization method which uses procedural personas to evaluate the playability and quality of evolved dungeon levels. Procedural personas represent archetypical player behaviors, and their controllers have been evolved to maximize a specific utility which drives their decisions. A “baseline” persona evaluates whether a level is playable by testing if it can survive in a worst-case scenario of the playthrough. On the other hand, a Monster Killer persona or a Treasure Collector persona evaluates playable levels based on how many monsters it can kill or how many treasures it can collect, respectively. Results show that the implemented two-population genetic algorithm discovers playable levels quickly and reliably, while the different personas affect the layout, difficulty level and tactical depth of the generated dungeons.

Stress Detection for PTSD via the StartleMart Game

Computer games have recently shown promise as a diagnostic and treatment tool for psychiatric rehabilitation. This paper examines the positive impact of affect detection and advanced game technology on the treatment of mental diagnoses such as Post Traumatic Stress Disorder (PTSD). For that purpose, we couple game design and game technology with stress detection for the automatic profiling and the personalized treatment of PTSD via game-based exposure therapy and stress inoculation training. The PTSD treatment game we designed forces the player to go through various stressful experiences while a stress detection mechanism profiles the severity and type of PTSD via skin conductance responses to those in-game stress elicitors. The initial study and analysis of 14 PTSD-diagnosed veteran soldiers presented in this paper reveals clear correspondence between diagnostic standard measures of PTSD severity and skin conductance responses. Significant correlations between physiological responses and subjective evaluations of the stressfulness of experiences, represented as pair wise preferences, are also found. We conclude that this supports the use of the simulation as a relevant treatment tool for stress inoculation training. This points to future avenues of research toward discerning between degrees and types of PTSD using game-based diagnostic and treatment tools.

Multimodal PTSD characterization via the StartleMart game

Computer games have recently shown promise as a diagnostic and treatment tool for psychiatric rehabilitation. This paper examines the potential of combining multiple modalities for detecting affective responses of patients interacting with a simulation built on game technology, aimed at the treatment of mental diagnoses such as post traumatic stress disorder (PTSD). For that purpose, we couple game design and game technology to create a game-based tool for exposure therapy and stress inoculation training that utilizes stress detection for the automatic profiling and potential personalization of PTSD treatments. The PTSD treatment game we designed forces the player to go through various stressful experiences while a stress detection mechanism profiles the severity and type of PTSD by analyzing the physiological responses to those in-game stress elicitors in two separate modalities: skin conductance (SC) and blood volume pulse (BVP). SC is often used to monitor stress as it is connected to the activation of the sympathetic nervous system (SNS). By including BVP into the model we introduce information about para-sympathetic activation, which offers a more complete view of the psycho-physiological experience of the player; in addition, as BVP is also modulated by SNS, a multimodal model should be more robust to changes in each modality due to particular drugs or day-to-day bodily changes. Overall, the study and analysis of 14 PTSD-diagnosed veteran soldiers presented in this paper reveals correspondence between diagnostic standard measures of PTSD severity and SC and BVP responsiveness and feature combinations thereof. The study also reveals that these features are significantly correlated with subjective evaluations of the stressfulness of experiences, represented as pairwise preferences. More importantly, the results presented here demonstrate that using the modalities of SC and BVP captures a more nuanced representation of player stress responses than using SC alone. We conclude that the results support the use of the simulation as a relevant treatment tool for stress inoculation training, and suggest the feasibility of using such a tool to profile PTSD patients. The use of multiple modalities appears to be key for an accurate profiling, although further research and analysis are required to identify the most relevant physiological features for capturing user stress.

Personas versus Clones for Player Decision Modeling

The current paper investigates how to model human play styles. Building on decision and persona theory we evolve game playing agents representing human decision making styles. Two methods are developed, applied, and compared: procedural personas, based on utilities designed with expert knowledge, and clones, trained to reproduce play traces. Additionally, two metrics for comparing agent and human decision making styles are proposed and compared. Results indicate that personas evolved from designer intuitions can capture human decision making styles equally well as clones evolved from human play traces.

Mechanics automatically recognized via interactive observation: jumping

Jumping has been an important mechanic since its introduction in Donkey Kong. It has taken a variety of forms and shown up in numerous games, with each jump having a different feel. In this paper, we use a modified Nintendo Entertainment System (NES) emulator to semi-automatically run experiments on a large subset (~30%) of NES platform games. We use these experiments to build models of jumps from different developers, series, and games across the history of the console. We then examine these models to gain insights into different forms of jumping and their associated feel.

Computational intelligence and cognitive performance assessment games

In this paper, we present the idea that game design, player modeling, and procedural content generation may offer new methods for modern psychological assessment, allowing for daily cognitive assessment in ways previously unseen. We suggest that games often share properties with psychological tests and that the overlap between the two domains might allow for creating games that contain assessment elements and provide examples from the literature that already show this. While approaches like these are typically seen as adding noise to a particular instrument in a psychometric context, research in player modeling demonstrates that it is possible to extract reliable measures corresponding to psychological constructs from in-game behavior and performance. Given these observations, we suggest that the combination of game design, player modeling, and procedural content generation offers new opportunities for conducting psychometric testing with a higher frequency and a higher degree of personalization than has previously been possible. Finally, we describe how we are currently implementing the first version of this vision in the form of an application for mobile devices that will soon be used in upcoming user studies.

To rank or to classify? Annotating stress for reliable PTSD profiling

In this paper we profile the stress responses of patients diagnosed with post-traumatic stress disorder (PTSD) to individual events in the game-based PTSD stress inoculation and exposure virtual environment StartleMart. Thirteen veterans suffering from PTSD play the game while we record their skin conductance. Game logs are used to identify individual events, and continuous decomposition analysis is applied to the skin conductance signals to derive event-related stress responses. The extracted skin conductance features from this analysis are used to profile each individual player in terms of stress response. We observe a large degree of variation across the 13 veterans which further validates the idiosyncratic nature of PTSD physiological manifestations. Further to game data and skin conductance signals we ask PTSD patients to indicate the most stressful event experienced (class-based annotation) and also compare the stress level of all events in a pairwise preference manner (rank-based annotation). We compare the two annotation stress schemes by correlating the self-reports to individual event-based stress manifestations. The self-reports collected through class-based annotation exhibit no correlation to physiological responses, whereas, the pairwise preferences yield significant correlations to all skin conductance features extracted via continuous decomposition analysis. The core findings of the paper suggest that reporting of stress preferences across events yields more reliable data that capture aspects of the stress experienced and that features extracted from skin conductance via continuous decomposition analysis offer appropriate predictors of stress manifestation across PTSD patients.

Evolving models of player decision making: Personas versus clones

Abstract The current paper investigates multiple approaches to modeling human decision making styles for procedural play-testing. Building on decision and persona theory we evolve game playing agents representing human decision making styles. Three kinds of agents are evolved from the same representation: procedural personas, evolved from game designer expert knowledge, clones, evolved from observations of human play and aimed at general behavioral replication, and specialized agents, also evolved from observation, but aimed at determining the maximal behavioral replication ability of the representation. These three methods are then compared on their ability to represent individual human decision makers. Comparisons are conducted using three different proposed metrics that address the problem of matching decisions at the action, tactical, and strategic levels. Results indicate that a small gallery of personas evolved from designer intuitions can capture human decision making styles equally well as clones evolved from human play-traces for the testbed game MiniDungeons.

Evolving game-specific UCB alternatives for general video game playing

At the core of the most popular version of the Monte Carlo Tree Search (MCTS) algorithm is the UCB1 (Upper Confidence Bound) equation. This equation decides which node to explore next, and therefore shapes the behavior of the search process. If the UCB1 equation is replaced with another equation, the behavior of the MCTS algorithm changes, which might increase its performance on certain problems (and decrease it on others). In this paper, we use genetic programming to evolve replacements to the UCB1 equation targeted at playing individual games in the General Video Game AI (GVGAI) Framework. Each equation is evolved to maximize playing strength in a single game, but is then also tested on all other games in our test set. For every game included in the experiments, we found a UCB replacement that performs significantly better than standard UCB1. Additionally, evolved UCB replacements also tend to improve performance in some GVGAI games for which they are not evolved, showing that improvements generalize across games to clusters of games with similar game mechanics or algorithmic performance. Such an evolved portfolio of UCB variations could be useful for a hyper-heuristic game-playing agent, allowing it to select the most appropriate heuristics for particular games or problems in general.

Games for Treating and Diagnosing Post Traumatic Stress Disorder

This chapter describes the use of games for addressing Post Traumatic Stress Disorder, a syndrome with a strong emotional component, characterized by among other things hyper-arousal. A number of games and game-like tools for treating Post Traumatic Stress Disorder are described. Subsequently, the chapter describes the design, development, and testing of a specific game for addressing Post Traumatic Stress Disorder which uses emotion recognition to characterize and target patient treatment. In clinical testing the game is found to elicit stress in patients suffering from Post Traumatic Stress Disorder based both on self-reports and physiological indicators of stress and arousal. Further, is shown that features extracted from the physiological signals can be combined with patient background information to predict measures of Post Traumatic Stress Disorder severity. This suggests that the combination of games and measurement of physiological indicators of emotional responses holds potential for creating novel tools for treating and diagnosing mental health disorders.