Impact of violent video game realism on the self-concept of aggressiveness assessed with explicit and implicit measures

We compared the standard 2D representation of a recent violent computer game to its 3D representation realized by shutter-goggles in a lab experiment. Assuming that the higher degree of realism of media violence would impact stronger on players in a pretest–posttest design, we analyzed the influence of violent video game exposure on implicit and explicit measures of aggressiveness. According to an explicit questionnaire on aggressiveness, participants reported having becoming more peaceful, whereas an Implicit Association Test on aggressiveness (Agg-IAT) indicated that the association between self and aggressive behavior became stronger after violence exposure, confirming the unique utility of Agg-IATs in media research. The 3D visualization mode, however, did not further strengthen this association, and a mediation model of increases in aggressiveness by participants’ flow experiences was not supported. When inspecting flow experiences, an interaction effect between gender and visualization mode was evident: Male participants were more likely to have flow experiences in the high-realism (3D) format, whereas female participants were more likely to experience flow in the standard (2D) mode. We discuss the findings in the context of automatic information processing in aggression, and we contend possible changes in automatic behavioral precursors due to media influence

Automatic Game Parameter Tuning using General Video Game Agents

Automatic Game Design is a subfield of Game Artificial Intelligence that aims to study the usage of AI algorithms for assisting in game design tasks. This dissertation presents a research work in this field, focusing on applying an evolutionary algorithm to video game parameterization. The task we are interested in is player experience. N-Tuple Bandit Evolutionary Algorithm (NTBEA) is an evolutionary algorithm that was recently proposed and successfully applied in game parameterization in a simple domain, which is the first experiment included in this project. To further investigating its ability in evolving game parameters, We applied NTBEA to evolve parameter sets for three General Video Game AI (GVGAI) games, because GVGAI has variety supplies of video games in different types and the framework has already been prepared for parameterization. 9 positive increasing functions were picked as target functions as representations of the player expected score trends. Our initial assumption was that the evolved games should provide the game environments that allow players to obtain score in the same trend as one of these functions. The experiment results confirm this for some functions, and prove that the NTBEA is very much capable of evolving GVGAI games to satisfy this task

Neurophysiological Assessment of Affective Experience

In the field of Affective Computing the affective experience (AX) of the user during the interaction with computers is of great interest. The automatic recognition of the affective state, or emotion, of the user is one of the big challenges. In this proposal I focus on the affect recognition via physiological and neurophysiological signals. Long‐standing evidence from psychophysiological research and more recently from research in affective neuroscience suggests that both, body and brain physiology, are able to indicate the current affective state of a subject. However, regarding the classification of AX several questions are still unanswered. The principal possibility of AX classification was repeatedly shown, but its generalisation over different task contexts, elicitating stimuli modalities, subjects or time is seldom addressed. In this proposal I will discuss a possible agenda for the further exploration of physiological and neurophysiological correlates of AX over different elicitation modalities and task contexts

Emphatic agents to reduce user frustration: The effects of varying agent characteristics

There is now growing interest in the development of computer systems which respond to users’ emotion and affect. We report three small scale studies (with a total of 42 participants) which investigate the extent to which affective agents, using strategies derived from human-human interaction, can reduce user frustration within human-computer interaction. The results confirm the previous findings of Klein et al (2002) that such interventions can be effective. We also obtained results that suggest that embodied agents can be more effective at reducing frustration than non-embodied agents, and that female embodied agents may be more effective than male embodied agents. These results are discussed in light of the existing research literature

Alleviation and Sanctions in Social Dilemma Games

This paper reports an experiment which compares behaviour in two punishment regimes: (i) a standard public goods game with punishment in which subjects are given the opportunity to punish other group members (democratic punishment regime) and (ii) a public goods game environment where all group members exogenously experience an automatic reduction of their income (irrespective of their behaviour) and are given the opportunity to alleviate the automatic penalty (undemocratic punishment regime). We employ a within-subjects design where subjects experience both environments and control for order effects by alternating their sequence. Our findings indicate that average contributions and earnings in the undemocratic punishment environment are significantly lower relative to the standard public goods game with punishment. We also observe that in the undemocratic environment average contributions decay over time only when subjects have experienced the standard public goods game with punishment. As a result, alleviation is significantly less when subjects have experienced the standard public goods game with punishment compared to when they do not have such experience. However, the assignment of punishment is robust irrespective of the order in which the games are played

Nonlinear Attitude Filtering: A Comparison Study

This paper contains a concise comparison of a number of nonlinear attitude filtering methods that have attracted attention in the robotics and aviation literature. With the help of previously published surveys and comparison studies, the vast literature on the subject is narrowed down to a small pool of competitive attitude filters. Amongst these filters is a second-order optimal minimum-energy filter recently proposed by the authors. Easily comparable discretized unit quaternion implementations of the selected filters are provided. We conduct a simulation study and compare the transient behaviour and asymptotic convergence of these filters in two scenarios with different initialization and measurement errors inspired by applications in unmanned aerial robotics and space flight. The second-order optimal minimum-energy filter is shown to have the best performance of all filters, including the industry standard multiplicative extended Kalman filter (MEKF)

Generating Levels That Teach Mechanics

The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.Comment: 8 pages, 7 figures, PCG Workshop at FDG 2018, 9th International Workshop on Procedural Content Generation (PCG2018