Description: The project explores the design and development of a 3D puzzle-based game, called May’s Journey, in which players solve an environmental maze by using the game’s pseudo code to manipulate game objects. The game is designed to teach introductory but foundational concepts of computer programming including abstraction, modularity, reusability, and debugging by focusing players […]
This project aims to the use a custom-built Alternate Reality Game (ARG) to assess the influence of individual differences on adaptability and teamwork in a digital gaming settings. It has three aims: To develop ARG-based quantitative computational measures that can assess adaptability and performance based on game data, to develop self-report measures that can characterize and measure adaptation processes and behaviours in a mixed-method way, and to validate all behaviour and adaptation measures resulting from this study in the real world.
Building a system to visualize and identify player as well as team behaviors and strategies.
Foldit is a revolutionary crowdsourcing computer game enabling you to contribute to important scientific research by folding proteins in a 3D puzzle game.
Building mixed-initiative (AI-assisted) tools that collaborate with designers to create interactive narrative scenarios.
Creating a mixed-initiative AI system to evolve new behavior from human-designed behavior trees in Unreal Engine 4.
We present an analysis of the academic landscape of games research from the last 15 years. We employed a data driven approach utilizing co-word and co-venue analysis on 48 core venues to identify 20 major research themes and 7 distinct communities, with a total of 8,207 articles and 21,552 unique keywords being analyzed. The results validated the commonly held assumption that games research has different clusters of papers and venues for technical versus nontechnical research, and identified interactions and synergies between these research clusters.
Using an Role-Playing Game (RPG) with multiple affordances, we designed an experiment collecting granular in-game behaviors of players. Using sequential pattern mining and supervised learning, we developed a model that uses gameplay action sequences to predict the real world characteristics, including gender, game play expertise and five personality traits (as defined by psychology). The results show that game expertise is a dominant factor that impacts in-game behaviors.
In this paper, we consider the problem of skill decomposition in MOBA (MultiPlayer Online Battle Arena) games, with the goal to understand what player skill factors are essential for the outcome of a game match. To understand the construct of MOBA player skills, we utilize various skill-based predictive models to decompose player skills into interpretative parts, the impact of which are assessed in statistical terms. We apply this analysis approach on two widely known MOBAs, namely League of Legends (LoL) and Defense of the Ancients 2 (DOTA2). The finding is that base skills of in-game avatars, base skills of players, and players’ champion-specific skills are three prominent skill components influencing LoL’s match outcomes, while those of DOTA2 are mainly impacted by in-game avatars’ base skills but not much by the other two.
Deck building is a crucial component in playing Collectible Card Games (CCGs). The goal of deck building is to choose a fixed-sized subset of cards from a large card pool, so that they work well together in-game against specific opponents. We propose a deck recommendation system, named Q-DeckRec, which requires small computational resources to recommend winning-effective decks after a training phase thus is suitable for real-time or large-scale application.