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.
Matchmaking connects multiple players to participate in online player-versus-player games. Current matchmaking systems depend on a single core strategy: create fair games at all times. These systems pair similarly skilled players on the assumption that a fair game is best player experience. We demonstrate, however, that this intuitive assumption sometimes fails and that matchmaking based on fairness is not optimal for engagement. Therefore, we propose an Engagement Optimized Matchmaking (EOMM) framework that maximizes overall player engagement. We prove that equal-skill based matchmaking is a special case of EOMM on a highly simplified assumption that rarely holds in reality. Our simulation on real data from a popular game made by Electronic Arts,Inc. (EA) supports our theoretical results, showing significant improvement in enhancing player engagement compared to existing matchmaking methods.
Hero drafting is a challenging problem in MOBA (MultiPlayer Online Battle Arena) games due to the complex hero-to-hero relationships to consider. We propose a novel hero recommendation system that suggests heroes to add to an existing team while maximizing the team’s prospect for victory. To that end, we model the drafting between two teams as a combinatorial game and use Monte Carlo Tree Search (MCTS) for estimating the values of hero combinations. Our empirical evaluation shows that hero teams drafted by our recommendation algorithm have significantly a higher win rate against teams constructed by other baseline and state-of-the-art strategies.