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Player experience and deceptive expectations of difficulty adaptation in digital games
Increasingly, digital games are including adaptive features that adjust the level of difficulty to match the skills of individual players. The intention is to improve and prolong the player experience by allowing the player to have the feeling of challenge without it being overwhelming and leading to repeated failure and frustration. Previous work has shown that player experience is indeed improved by such adaptations but also that the player experience can be improved by simply claiming such an adaptation is present even when it is not. It is therefore possible that claims about adaptations and the actual adaptations could interact and not lead to the intended outcomes for the players or worse disappoint players. This paper reports on two studies that were conducted to experimentally investigate the interaction between game adaptations and player information about adaptations on the player experience, specifically their sense of immersion in the game. For this, two games were developed using two different kinds of adaptations to adjust difficulty based on players’ performance in the game. Participants were provided with information about game adaptations independently of whether the adaptations were present. The results suggest that players felt more immersed in the game when told that the game adapts to them, regardless of whether the adaptation was present in the game or not. This effect was observed in both games despite their different adaptations and it remained prominent even during longer gaming sessions. These findings demonstrate that players’ knowledge of adaptations influences their experience independently of adaptations. In this particular context, the knowledge reinforced the experience of the adaptations. This suggests that, at least in some circumstances, developers do not need to be concerned about negative effects of telling players about in-game adaptations
A Mixed Method Approach for Evaluating and Improving the Design of Learning in Puzzle Games
Despite the acknowledgment that learning is a necessary part of all gameplay, the area of Games User Research lacks an established evidence based method through which designers and researchers can understand, assess, and improve how commercial games teach players game-specific skills and information. In this paper, we propose a mixed method procedure that draws together both quantitative and experiential approaches to examine the extent to which players are supported in learning about the game world and mechanics. We demonstrate the method through presenting a case study of the game Portal involving 14 participants, who differed in terms of their gaming expertise. By comparing optimum solutions to puzzles against observed player performance, we illustrate how the method can indicate particular problems with how learning is structured within a game. We argue that the method can highlight where major breakdowns occur and yield design insights that can improve the player experience with puzzle games
Just war? War games, war crimes, and game design
Military shooters have explored both historical and modern settings and remain one of the most popular game genres. While the violence of these games has been explored in multiple studies, the study of how war and the rules of war are represented is underexplored. The Red Cross has argued that as virtual war games are becoming closer to reality, the rules of war should be included. This article explores the argument put forward by the Red Cross and its reception by games media organizations, in order to consider how the concept of “just war” is represented within games. This article will focus on concerns over games adherence to the criteria of jus in bello (the right conduct in war) and will also consider the challenges that developers face in the creation of entertainment products in the face of publisher and press concerns
Comparing dynamitic difficulty adjustment and improvement in action game
A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Master ResearchDesigning a game difficulty is one of the key things as a game designer. Player will be feeling boring when the game designer makes the game too easy or too hard. In the past decades, most of single player games can allow players to choose the game difficulty either easy, normal or hard which define the overall game difficulty. In action game, these options are lack of flexibility and they are unsuitable to the player skill to meet the game difficulty. By using Dynamic Difficulty Adjustment (DDA), it can change the game difficulty in real time and it can match different player skills. In this paper, the final goal is the comparison of the three DDA systems in action game and apply an improved DDA. In order to apply a new improved DDA, this thesis will evaluate three chosen DDA systems with chosen action decision based AI for action game. A new DDA measurement formula is applied to the comparing section
Bacteria Hunt: A multimodal, multiparadigm BCI game
Brain-Computer Interfaces (BCIs) allow users to control applications by brain activity. Among their possible applications for non-disabled people, games are promising candidates. BCIs can enrich game play by the mental and affective state information they contain. During the eNTERFACE’09 workshop we developed the Bacteria Hunt game which can be played by keyboard and BCI, using SSVEP and relative alpha power. We conducted experiments in order to investigate what difference positive vs. negative neurofeedback would have on subjects’ relaxation states and how well the different BCI paradigms can be used together. We observed no significant difference in mean alpha band power, thus relaxation, and in user experience between the games applying positive and negative feedback. We also found that alpha power before SSVEP stimulation was significantly higher than alpha power during SSVEP stimulation indicating that there is some interference between the two BCI paradigms
Picking pockets on the lawn: the development of tactics and strategies in a mobile game
This paper presents Treasure, an outdoor mobile multiplayer game inspired by Weiser’s notion of seams, gaps and breaks in different media. Playing Treasure involves movement in and out of a wi-fi network, using PDAs to pick up virtual ’coins’ that may be scattered outside network coverage. Coins have to be uploaded to a server to gain game points, and players can collaborate with teammates to double the points given for an upload. Players can also steal coins from opponents. As they move around, players’ PDAs sample network signal strength and update coverage maps. Reporting on a study of players taking part in multiple games, we discuss how their tactics and strategies developed as their experience grew with successive games. We suggest that meaningful play arises in just this way, and that repeated play is vital when evaluating such games
Argotario: Computational Argumentation Meets Serious Games
An important skill in critical thinking and argumentation is the ability to
spot and recognize fallacies. Fallacious arguments, omnipresent in
argumentative discourse, can be deceptive, manipulative, or simply leading to
`wrong moves' in a discussion. Despite their importance, argumentation scholars
and NLP researchers with focus on argumentation quality have not yet
investigated fallacies empirically. The nonexistence of resources dealing with
fallacious argumentation calls for scalable approaches to data acquisition and
annotation, for which the serious games methodology offers an appealing, yet
unexplored, alternative. We present Argotario, a serious game that deals with
fallacies in everyday argumentation. Argotario is a multilingual, open-source,
platform-independent application with strong educational aspects, accessible at
www.argotario.net.Comment: EMNLP 2017 demo paper. Source codes:
https://github.com/UKPLab/argotari
Deep reinforcement learning from human preferences
For sophisticated reinforcement learning (RL) systems to interact usefully
with real-world environments, we need to communicate complex goals to these
systems. In this work, we explore goals defined in terms of (non-expert) human
preferences between pairs of trajectory segments. We show that this approach
can effectively solve complex RL tasks without access to the reward function,
including Atari games and simulated robot locomotion, while providing feedback
on less than one percent of our agent's interactions with the environment. This
reduces the cost of human oversight far enough that it can be practically
applied to state-of-the-art RL systems. To demonstrate the flexibility of our
approach, we show that we can successfully train complex novel behaviors with
about an hour of human time. These behaviors and environments are considerably
more complex than any that have been previously learned from human feedback
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