Bardia Aghabeigi

Understanding and evaluating cooperative games

Cooperative design has been an integral part of many games. With the success of games like Left4Dead, many game designers and producers are currently exploring the addition of cooperative patterns within their games. Unfortunately, very little research investigated cooperative patterns or methods to evaluate them. In this paper, we present a set of cooperative patterns identified based on analysis of fourteen cooperative games. Additionally, we propose Cooperative Performance Metrics (CPM). To evaluate the use of these CPMs, we ran a study with a total of 60 participants, grouped in 2-3 participants per session. Participants were asked to play four cooperative games (Rock Band 2, Lego Star Wars, Kameo, and Little Big Planet). Videos of the play sessions were annotated using the CPMs, which were then mapped to cooperative patterns that caused them. Results, validated through inter-rater agreement, identify several effective cooperative patterns and lessons for future cooperative game designs.

The Effect of Age, Gender, and Previous Gaming Experience on Game Play Performance

It is common sense that people don’t play games that are too difficult for them. Thus Game developers need to understand the performance abilities of players. Several studies suggest a clear dissimilarity in video game playing abilities between different genders and age groups. In this paper, we report on a study investigating impact of age, gender and previous gaming experience on gameplay performance. The study explored the performance of 60 kids 6-16 years old within three video games: Rock Band 2, Lego Star Wars and Kameo. The paper outlines clear impact of age and gender and less prior gaming experience on performance parameters: score and game progression.

User studies: a strategy towards a successful industry-academic relationship

Game industry-academic relationships are traditionally related to technology development and education, but more research-oriented partnerships outside of direct technology development and education are forming. With these types of partnerships come stumbling blocks that must be resolved for successful outcomes. Meanwhile, user-oriented research is becoming an essential component in game production due to its utility in guiding the quality of game products. Academia can help inform user studies, which calls for industry-academic partnerships. This opportunity has enabled and stimulated the collaboration between Simon Fraser University and Bardel Entertainment in Vancouver, British Columbia. This paper discusses the importance of game industry and academic collaboration, current opportunities, and strategies based on the SFU-Bardel partnership. Two in-progress projects are detailed: developing novel user testing methods and guidance on design through navigation analysis and playtesting sessions.

Similarity in visual designs: effects on workload and performance in a railed-shooter game

Games are a popular form of digital entertainment and one elusive question is how complex visual designs affect the player experience. We address one aspect of this topic in terms of similarity of visual features, explored both as an organizing principle in Gestalt psychology and as a theory in visual attention. To address this issue, we developed a 3D railed shooter game with adjustable visual features of size, speed, and density of targets and non-targets. Based on these features we evaluate 105 players performance in 4 visual conditions. In addition, we employ a cognitive workload assessment as a means to understand the perceived demands on players. Results show effects of expertise on performance and cognitive workload, per visual condition. Our methods and implications on game design are discussed.

Assistive design and production in computer games: Parametric systems, data mining, visual analytics

Real Time Strategy (RTS) games are complex, often requiring modeling of economic behaviors to develop a successful game. The design process for developing such an RTS game usually involves designers developing rules and mechanics based on their intuition and then revising these rules over several successive iterations to ensure that the model works for the target market. For the past year, we have been collaborating with a game company (BlackBird Interactive Inc.) to create an economy model to assist in designing a game. BlackBird is developing a social multiplayer RTS game called HARDWARE. Our goal is to develop assistive technologies to allow designers to make informed decisions regarding their game mechanics. In this paper we discuss the approach we took and our method to validate it in future. In particular, we developed an innovative technique using simulation and behavior modeling to allow designers to better adjust and understand the game variables. In this paper we discuss the model and preliminary interactions with the design team to show the success of this model. Our model describes how the game economy should unfold in different dimensions. We considered several parameters for user behavior, including propensity factors (such as their tendency towards certain game activities). We then developed a simulation system using Matlab and Simulink which considers the developed parameterized mathematical model as input, and uses a stochastic Monte Carlo approach for running probability based equations. The system extracts an empirical distribution for each model output variable over time. These output variables can represent how the economy resources change for a specific class of users.

Visual Analytics Tools – A Lens into Player’s Temporal Progression and Behavior

As argued in previous chapters, developing engaging interactive new media experiences, including virtual worlds, multi-player or single-player games, involves understanding the target market. Telemetry and metrics can provide a powerful method to enable designers and other industry professionals to gain insight about their users. Previous chapters have discussed telemetry analysis extensively (see Chaps. 4, 5, 7, 9, 11, 12, 13, 14, 15, 16, and 17). Chapter 18 introduced the Information visualization field and game visual analytics, specifically. In this chapter, we extend the arguments made in previous chapters, specifically investigating the use of visual analytics systems that reveal player behaviors over time, where we emphasize the temporal dimension as key to revealing information that indicate causes for specific behavior, and they may also give developers more insight about popular patterns of behaviors.

Body buddies: social signaling through puppeteering

While virtual worlds have evolved to provide a good medium for social communication, they are very primitive in their social and affective communication design. The social communication methods within these worlds have progressed from early text-based social worlds, e.g. MUDS (multi-user dungeons) to 3D graphical interfaces with avatar control, such as Second Life. Current communication methods include triggering gestures by typed commands, and/or selecting a gesture by name through the user interface. There are no agreed-upon standards for organizing such gestures or interfaces. In this paper, we address this problem by discussing a Unity-based avatar pupeteering prototype we developed called Body Buddies. Body Buddies sits on top of the communication program Skype, and provides additional modalities for social signaling through avatar pupeteering. Additionally, we discuss results from an exploratory study we conducted to investigate how people use the interface. We also outline steps to continuously develop and evolve Body Buddies.

Evolutionary Design of a Fuzzy Rule Base for Solving the Goal-Shooting Problem in the RoboCup 3D Soccer Simulation League

Most of the problems in the RoboCup soccer domain suffer from the noisy perceptions, noisy actions, and continuous state space. To cope with these problems, using Fuzzy logic can be a proper choice, due to its capabilities of inferring and approximate reasoning under uncertainty. However, designing the entire rule base of a Fuzzy rule base system (FRBS) by an expert is a boring and time consuming task and sometimes the performance of the designed Fuzzy system is far from the optimum, especially in cases that the available knowledge of the system is not enough. In this paper, a rule learning method based on the iterative rule learning (IRL) approach is proposed to generate the entire rule base of an FRBS with the help of genetic algorithms (GAs). The advantage of our proposed method compared to similar approaches in the literature is that our algorithm does not need any training set, which is difficult to collect in many cases; cases like most of the problems existing in the RoboCup soccer domain. As a test case, the goal-shooting problem in the RoboCup 3D soccer simulation league is chosen to be solved using this approach. Simulation tests reveal that with applying the rule learning method proposed in this paper on the goal-shooting problem, not only can a rule base with good performance in goal-shooting skill be obtained, but also the number of rules in the rule base can be decreased by using the general rules in constructing the rule base.