Computer-controlled Characters Research Articles

The Influence of Social Competition and Maths Anxiety on Game Performance

Digital Game-Based Learning (DGBL) uses serious games to deliver educational content. DGBL has the potential to aid children’s development of maths skills, providing educators with alternatives to traditional mathematics teaching. However, the efficacy of serious games can be affected by their attributes, including whether they are single or multi-player. While competition in a multi-player environment may enhance players’ interest and motivation, potential social pressure may lead to anxiety and affect performance. In maths education, there are additional challenges. Mathematics Anxiety (MA), the negative emotional response to mathematics, may interact with game features and impact players’ experience. This study assesses players' performance in a digital maths game called ’Seven Spells’ across two different game modes (vs. Human and vs. CPU), and it also investigates whether MA levels impacted players’ performance. 43 children from two classes in an Irish primary school participated in thisstudy. Each class played a different game mode of the ‘Seven Spells’ game, one group playing against computer-controlled characters (vs. CPU) and the other playing against their classmates (vs. Human). The game mechanics were identical between the two competition game modes. The children completed the modified Abbreviated Mathematics Anxiety Scale (mAMAS) and a mathematics questionnaire before participating in the intervention, which included 4 sessions. They also participated in a focus group session at the end of the intervention. Data on their gameplay were collected throughout the first three sessions and analysed through multiple linear regression models to investigate possible connections between MA, maths knowledge, game modes and game performance. The results of the multiple linear regressions indicate that game performance was positively influenced by the duration of gameplay and the complexity of moves — measured by the number of moves and cards used — while being negatively affected by MA. However, a significant interaction between MA and the competition game mode was observed, suggesting that MA had a more pronounced negative impact on players in the vs. Human game mode compared to the vs. CPU game mode.

Wayfinding in immersive virtual environments as social activity supported by virtual agents

Effective navigation and interaction within immersive virtual environments (IVEs) rely on thorough scene exploration. Therefore, wayfinding is essential, assisting users in comprehending their surroundings, planning routes, and making informed decisions. Based on real-life observations, wayfinding is, thereby, not only a cognitive process but also a social activity profoundly influenced by the presence and behaviors of others. In virtual environments, these “others” are virtual agents (VAs), defined as anthropomorphic computer-controlled characters, who enliven the environment and can serve as background characters or direct interaction partners. However, less research has been conducted to explore how to efficiently use VAs as social wayfinding support. In this paper, we aim to assess and contrast user experience, user comfort, and acquisition of scene knowledge through a between-subjects study involving n = 60 participants across three distinct wayfinding conditions in one slightly populated urban environment: (i) unsupported wayfinding, (ii) strong social wayfinding using a virtual supporter who incorporates guiding and accompanying elements while directly impacting the participants’ wayfinding decisions, and (iii) weak social wayfinding using flows of VAs that subtly influence the participants’ wayfinding decisions by their locomotion behavior. Our work is the first to compare the impact of VAs’ behavior in virtual reality on users’ scene exploration, including spatial awareness, scene comprehension, and comfort. The results show the general utility of social wayfinding support, while underscoring the superiority of the strong type. Nevertheless, further exploration of weak social wayfinding as a promising technique is needed. Thus, our work contributes to the enhancement of VAs as advanced user interfaces, increasing user acceptance and usability.

Association of Game Events with Facial Animations of Computer-Controlled Virtual Characters Based on Probabilistic Human Reaction Modeling

Computer-controlled virtual characters are essential parts of most virtual environments and especially computer games. Interaction between these virtual agents and human players has a direct impact on the believability of and immersion in the application. The facial animations of these characters are a key part of these interactions. The player expects the elements of the virtual world to act in a similar manner to the real world. For example, in a board game, if the human player wins, he/she would expect the computer-controlled character to be sad. However, the reactions, more specifically, the facial expressions of virtual characters in most games are not linked with the game events. Instead, they have pre-programmed or random behaviors without any understanding of what is really happening in the game. In this paper, we propose a virtual character facial expression probabilistic decision model that will determine when various facial animations should be played. The model was developed by studying the facial expressions of human players while playing a computer videogame that was also developed as part of this research. The model is represented in the form of trees with 15 extracted game events as roots and 10 associated animations of facial expressions with their corresponding probability of occurrence. Results indicated that only 1 out of 15 game events had a probability of producing an unexpected facial expression. It was found that the “win, lose, tie” game events have more dominant associations with the facial expressions than the rest of game events, followed by “surprise” game events that occurred rarely, and finally, the “damage dealing” events.

Coping with opponents: multi-objective evolutionary neural networks for fighting games

Fighting games represent a challenging problem for computer-controlled characters. Therefore, they have attracted considerable research interest. This paper investigates novel multi-objective neuroevolutionary approaches for fighting games focusing on the Fighting Game AI Competition. Considering several objectives shall improve the AI’s generalization capabilities when confronted with new opponents.To this end, novel combinations of neuroevolution and multi-objective evolutionary algorithms are explored. Since the variants proposed employ the well-known R2 indicator, we derived a novel faster algorithm for determining the exact R2 contribution. An experimental comparison of the novel variants to existing multi-objective neuroevolutionary algorithms demonstrates clear performance benefits on the test case considered. The best performing algorithm is then used to evolve controllers for the fighting game. Comparing the results with state-of-the-art AI opponents shows very promising results; the novel bot is able to outperform several competitors.

Effects of Human vs. Computer-Controlled Characters and Social Identity Cues on Enjoyment

Abstract. This study explored how group identification, avatar similarity identification, and social presence mediated the effect of character type (avatars or agents) and social identity cues (presence or absence of avatars wearing participants’ school colors) on game enjoyment. Playing with teammate avatars increased enjoyment indirectly by enhancing group identification. In addition, the presence of social identity cues increased enjoyment indirectly by augmenting identification with one’s avatar. Unexpectedly, playing in multiplayer mode in the presence of social identity cues decreased enjoyment, whereas playing in multiplayer mode in the absence of social identity cues increased enjoyment. Social presence was not a reliable mediator. The findings supported media enjoyment and social identity theories, and highlighted how virtual character type and identification processes influence enjoyment.

A new solution to the gamer’s dilemma

Luck (2009) argues that gamers face a dilemma when it comes to performing certain virtual acts. Most gamers regularly commit acts of virtual murder, and take these acts to be morally permissible. They are permissible because unlike real murder, no one is harmed in performing them; their only victims are computer-controlled characters, and such characters are not moral patients. What Luck points out is that this justification equally applies to virtual pedophelia, but gamers intuitively think that such acts are not morally permissible. The result is a dilemma: either gamers must reject the intuition that virtual pedophelic acts are impermissible and so accept partaking in such acts, or they must reject the intuition that virtual murder acts are permissible, and so abstain from many (if not most) extant games. While the prevailing solution to this dilemma has been to try and find a morally relevant feature to distinguish the two cases, I argue that a different route should be pursued. It is neither the case that all acts of virtual murder are morally permissible, nor are all acts of virtual pedophelia impermissible. Our intuitions falter and produce this dilemma because they are not sensitive to the different contexts in which games present virtual acts.

The Others Will Help: The Presence of Multiple Video Game Characters Reduces Helping After the Game Is Over

The bystander effect refers to the phenomenon that individuals are less likely to help if there are potential other helpers present. For instance, past research revealed that participants were less likely to help computer-controlled characters if there were other computer-controlled characters present. Research has also shown that the bystander effect occurs if the presence of others is merely imagined. The present research examined the idea that the presence of multiple characters within a video game reduces the player's helping behavior even after the video game is over. In fact, participants who played a video game with multiple characters present were less likely to devote time to assisting in a future study than participants who had played the same video game with only a single character present.

Pals in Power Armor: Attribution of Human-Like Emotions to Video Game Characters in an Ingroup/Outgroup Situation

Previous research has demonstrated the tendency for humans to anthropomorphize computers-that is, to react to computers as social actors, despite knowing that the computers are mere machines. In the present research, we examined the attribution of both primary (non-uniquely human) and secondary (human-like) emotions to ingroup (teammate) and outgroup (opponent) computer-controlled characters in a video game. We found that participants perceived the teammate character as experiencing more secondary emotions than the opponent character, but that they perceived the teammate and opponent character as experiencing equal levels of primary emotions. Thus, participants anthropomorphized the ingroup character to a greater extent than the outgroup character. These results imply that computers' "emotions" are treated with a similar ingroup/outgroup social regard as the emotions of actual humans.

AIPaint: A Sketch-Based Behavior Tree Authoring Tool

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.

A platform for evolving intelligently interactive adversaries

Entertainment software developers face significant challenges in designing games with broad appeal. One of the challenges concerns creating nonplayer (computer-controlled) characters that can adapt their behavior in light of the current and prospective situation, possibly emulating human behaviors. This adaptation should be inherently novel, unrepeatable, yet within the bounds of realism. Evolutionary algorithms provide a suitable method for generating such behaviors. This paper provides background on the entertainment software industry, and details a prior and current effort to create a platform for evolving nonplayer characters with genetic and behavioral traits within a World War I combat flight simulator.

Human-Level AI's Killer Application: Interactive Computer Games

Although one of the fundamental goals of AI is to understand and develop intelligent systems that have all the capabilities of humans, there is little active research directly pursuing this goal. We propose that AI for interactive computer games is an emerging application area in which this goal of human-level AI can successfully be pursued. Interactive computer games have increasingly complex and realistic worlds and increasingly complex and intelligent computer-controlled characters. In this article, we further motivate our proposal of using interactive computer games for AI research, review previous research on AI and games, and present the different game genres and the roles that human-level AI could play within these genres. We then describe the research issues and AI techniques that are relevant to each of these roles. Our conclusion is that interactive computer games provide a rich environment for incremental research on human-level AI.