RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

The balance of game content significantly impacts the gaming experience. Unbalanced game content diminishes engagement or increases frustration because of repetitive failure. Although game designers intend to adjust the difficulty of game content, this is a repetitive, labor-intensive, and challenging process, especially for commercial-level games with extensive content. To address this issue, the game research community has explored automated game balancing using artificial intelligence (AI) techniques. However, previous studies have focused on limited game content and did not consider the importance of the generalization ability of playtesting agents when encountering content changes. In this study, we propose RaidEnv, a new game simulator that includes diverse and customizable content for the boss raid scenario in MMORPG games. Additionally, we design two benchmarks for the boss raid scenario that can aid in the practical application of game AI. These benchmarks address two open problems in automatic content balancing, and we introduce two evaluation metrics to provide guidance for AI in automatic content balancing. This novel game research platform expands the frontiers of automatic game balancing problems and offers a framework within a realistic game production pipeline.Comment: 14 pages, 6 figures, 6 tables, 2 algorithm

Artificial intelligence in co-operative games with partial observability

This thesis investigates Artificial Intelligence in co-operative games that feature Partial Observability. Most video games feature a combination of both co-operation, as well as Partial Observability. Co-operative games are games that feature a team of at least two agents, that must achieve a shared goal of some kind. Partial Observability is the restriction of how much of an environment that an agent can observe. The research performed in this thesis examines the challenge of creating Artificial Intelligence for co-operative games that feature Partial Observability. The main contributions are that Monte-Carlo Tree Search outperforms Genetic Algorithm based agents in solving co-operative problems without communication, the creation of a co-operative Partial Observability competition promoting Artificial Intelligence research as well as an investigation of the effect of varying Partial Observability to Artificial Intelligence, and finally the creation of a high performing Monte-Carlo Tree Search agent for the game Hanabi that uses agent modelling to rationalise about other players

Multiplayer Tension in the Wild: A Hearthstone Case

Games are designed to elicit strong emotions during game play, especially when players are competing against each other. Artificial Intelligence applied to predict a player's emotions has mainly been tested on single-player experiences in low-stakes settings and short-term interactions. How do players experience and manifest affect in high-stakes competitions, and which modalities can capture this? This paper reports a first experiment in this line of research, using a competition of the video game Hearthstone where both competing players' game play and facial expressions were recorded over the course of the entire match which could span up to 41 minutes. Using two experts' annotations of tension using a continuous video affect annotation tool, we attempt to predict tension from the webcam footage of the players alone. Treating both the input and the tension output in a relative fashion, our best models reach 66.3% average accuracy (up to 79.2% at the best fold) in the challenging leave-one-participant out cross-validation task. This initial experiment shows a way forward for affect annotation in games "in the wild"in high-stakes, real-world competitive settings

"The Collecting Itself Feels Good": Towards Collection Interfaces for Digital Game Objects

© Lennart Nacke, 2016. This is the author’s version of the work. It is posted here for your personal use. Not for redistribution. The definitive version was published in CHI PLAY Companion '16 Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts, https://doi.org/10.1145/2967934.2968088Digital games offer a variety of collectible objects. We investigate players' collecting behaviors in digital games to determine what digital game objects players enjoyed collecting and why they valued these objects. Using this information, we seek to inform the design of future digital game object collection interfaces. We discuss the types of objects that players prefer, the reasons that players value digital game objects, and how collection behaviors may guide play. Through our findings, we identify design implications for digital game object collection interfaces: enable object curation, preserve rules and mechanics, preserve context of play, and allow players to share their collections with others. Digital game object collection interfaces are applicable to the design of digital games, gamified applications, and educational software.Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada Social Sciences and Humanities Research Council of CanadaPeer-reviewe

Developing a Serious Game to Explore Joint All Domain Command and Control

Changes in the geopolitical landscape and increasing technological complexity have prompted the U.S. Military to coin Multi-Domain Operations (MDO) and Joint All-Domain Command and Control as terms to describe an over-arching strategy that frames the complexity of warfare across both traditional and emerging warfighting domains. Teaching new and advanced concepts associated with these terms requires both innovation as well as distinct education and training tools in order to realize the cultural change advocated by senior military leaders. BSN, a Collectible Card Game, was developed to teach concepts integral to MDO and initiate discussion on military strategy

Developing and Assessing a Workshop That Utilizes a Serious Game to Introduce Joint All-domain Operations

The DoD has begun developing Joint All-Domain Operations (JADO) to prepare for the future of warfare. As complexity and technological capability increases, the U.S. military needs to adapt to provide a more lethal and capable force, able to compete and win against near-peer adversaries. This research describes the development of an Introduction to JADO Workshop designed to provide a structured primer into JADO concepts. The research also presents an extension of BSN in the form of BSN scenarios. These scenarios alter the rules to lessen the learning curve for the game and to engage with JADO concepts. This research proposed a format for future JADO education course, refined the BSN tool to improve effectiveness, measurement of the response to JADO education, and an assessment of the workshop from JADO leaders across the Air Force

Introducing the Game Design Matrix: A Step-by-Step Process for Creating Serious Games

The Game Design Matrix makes effective game design accessible to novice game designers. Serious Games are a powerful tool for educators seeking to boost the level of student engagement and application in academic environments, but the can be difficult to incorporate into existing courses due to availability and the cost of quality game design. The Game Design Matrix was used by two educators, novice game designers, to create a serious game. The games were assessed in an academic setting and observed to be effective in engagement, interaction, and achieving higher levels of learning