HeapCraft Social Tools: Understanding and Improving Player Collaboration in Minecraft

We introduce a framework to infuence and analyze player collaboration in Minecraft. The framework consists of a telemetry system and several tools to influence player behavior and provide value to server administrators to increase adoption. The data collection includes almost every aspect of gameplay and can be used for analysis beyond player collaboration.1 We started collecting data from several Minecraft servers in March 2015. Most data will be made available to researchers upon request.2 We have also demonstrated the use of our framework to statistically analyze player behavior in Minecraft. More details can be found [1]

HEAPCRAFT: Quantifying and Predicting Collaboration in Minecraft

We present HEAPCRAFT: an open-source suite of tools for monitoring and improving collaboration in Minecraft. At the core of our system is a data collection and analysis framework or recording gameplay. We collected over 3451 playerhours of game behavior from 908 different players, and performed a general study of online collaboration. To make our game analytics easily accessible, we developed interactive information visualization tools and an analysis framework for players, administrators, and researchers to explore graphs, maps and timelines of live server activity. As part of our research, we introduce the collaboration index, a metric which allows server administrators and researchers to quantify, predict, and improve collaboration on Minecraft servers. Our analysis reveals several possible predictors of collaboration which can be used to improve collaboration on Minecraft servers. HEAPCRAFT is designed to be general, and has the potential to be used for other shared online virtual worlds

MPM based simulation for various solid deformation

Solid materials are responsible for many interesting phenomena. There are various types of them such as deformable objects and granular materials. In this paper, we present an MPM based framework to simulate the wide range of solid materials. In this framework, solid mechanics is based on the elastoplastic model, where we use von Mises criterion for deformable objects, and the Drucker-Prager model with non-associated plastic flow rules for granular materials. As a result, we can simulate different kinds of deformation of deformable objects and sloping failure for granular materials

Statistical Analysis of Player Behavior in Minecraft

Interactive Virtual Worlds offer new individual and social experiences in a huge variety of artificial realities. They also have enormous potential for the study of how people interact, and how societies function and evolve. Systematic collection and analysis of in-play behavioral data will be invaluable for enhancing player experiences, facilitating effective administration, and unlocking the scientific potential of online societies. This paper details the development of a framework to collect player data in Minecraft. We present a complete solution which can be deployed on Minecraft servers to send collected data to a centralized server for visualization and analysis by researchers, players, and server administrators. Using the framework, we collected and analyzed over 14 person-days of active gameplay. We built a classification tool to identify high-level player behaviors from observations of their moment-by-moment game actions. Heat map visualizations highlighting spatial behavior can be used by players and server administrators to evaluate game experiences. Our data collection and analysis framework offers the opportunity to understand how individual behavior, environmental factors, and social systems interact through large-scale observational studies of virtual worlds

Adaptive Sampling and Rendering of Fluids on the GPU

In this paper, we propose a novel GPU-friendly algorithm for the Smoothed Particle Hydrodynamics (SPH) simulation for weakly compressible fluids. The major goal of our algorithm is to implement a GPU-based SPH simulation that can simulate and render a large number of particles at interactive speed. Additionally, our algorithm exhibits the following three features. Firstly, our algorithm supports adaptive sampling of the fluids. Particles can be split into several sub-particles in geometrically complex regions to provide a more accurate simulation. At the same time, nearby particles deep inside the fluids are merged to a single particle to reduce the number of particles. Secondly, the fluids are visualized by directly computing the intersection between ray and an isosurface defined by the surface particles. A dynamic particle grouping algorithm and equation solver are employed to quickly find the ray-isosurface intersection. Thirdly, based on the observation that the SPH simulation is a naturally parallel algorithm, the whole SPH simulation, including the adaptive sampling of the fluids as well as surface particle rendering, is executed on the GPU to fully utilize the computational power and parallelism of modern graphics hardware. Our experimental data shows that we can simulate about 50K adaptively sampled particles, or up to 120K particles in the fixed sampling case at a rate of approximately 20 time steps per second

Efficient refinement of dynamic point data

Particle simulations as well as geometric modeling techniques have demonstrated their ability to process and render points interactively. However, real-time particle-based fluid simulations suffer from poor rendering quality due to low surface particle resolutions. Surfaces appear blobby, surface details are lost, and features like edges are degraded due to smoothing effects. This paper presents a novel point refinement method for irregularly sampled, dynamic points coming from a particle-based fluid simulation. Our interpolation algorithm can handle complex geometries including splashes, and at the same time preserves features like edges. Point collisions are avoided resulting in a nearly uniform sampling facilitating surface reconstruction techniques. No point preprocessing is necessary, and point neighborhoods are dynamically updated reducing computation and memory costs. We show that our algorithm can efficiently detect and refine the surface points of a fluid and we demonstrate the improvement of rendering quality and applicability to real-time simulations

Interactive SPH simulation and rendering on the GPU

In this paper we introduce a novel parallel and interactive SPH simulation and rendering method on the GPU using CUDA which allows for high quality visualization. The crucial particle neighborhood search is based on Z-indexing and parallel sorting which eliminates GPU memory overhead due to grid or hierarchical data structures. Furthermore, it overcomes limitations imposed by shading languages allowing it to be very flexible and approaching the practical limits of modern graphics hardware. For visualizing the SPH simulation we introduce a new rendering pipeline. In the first step, all surface particles are efficiently extracted from the SPH particle cloud exploiting the simulation data. Subsequently, a partial and therefore fast distance field volume is rasterized from the surface particles. In the last step, the distance field volume is directly rendered using state-of-the-art GPU raycasting. This rendering pipeline allows for high quality visualization at very high frame rates

Investigating the effect of academic procrastination on the frequency and variety of academic misconduct: A panel study

Patrzek J, Sattler S, van Veen F, Grunschel C, Fries S. Investigating the effect of academic procrastination on the frequency and variety of academic misconduct: A panel study. Studies in Higher Education. 2015;40(6):1014-1029