An adaptive training framework for increasing player proficiency in games and simulations

To improve a player's proficiency at a particular video game, the player must be presented with an appropriate level of challenge. This level of challenge must remain relative to the player as their proficiency changes. The current fixed difficulty settings (e.g. easy, medium or hard) provide a limited range of difficulty for the player. This work aims to address this problem through developing an adaptive training framework that utilities existing work in Dynamic Difficulty Adjustment to construct an adaptive AI opponent. The framework also provides a way to measure the player's proficiency, by analysing the level of challenge the adaptive AI opponent provides for the player. This work tests part of the proposed adaptive training framework through a pilot study that uses a real-time fighting game. Copyright is held by the owner/author(s)

A Review of Agency Architectures in Interactive Drama Systems

This paper provides a review of the interactive drama field, attempting to create a taxonomy for classifying system affordances for emergence and authorial control, referred to in this paper as "agency architecture". The classification of interactive drama systems according to a spectrum of agency architectures, helps to identify open questions in the field, providing a better understanding of which architectures would benefit from greater attention from the research community. In this paper, several key interactive drama systems from the field's literature are categorised by agency architecture. This is followed by a summary and analysis of their architectural classifications, alongside justifications for their assigned categories. It is then concluded with the identification of a number of research gaps, revealed by the compiled classifications, that highlight potential future research questions

Last Island: Exploring Transitions to Sustainable Futures through Play

© 2019 Association for Computing Machinery. A serious game was designed and developed with the goal of exploring potential sustainable futures and the transitions towards them. This computer-assisted board game, Last Island, which incorporates a system dynamics model into a board game's core mechanics, attempts to impart knowledge and understanding on sustainability and how an isolated society may transition to various futures to a non-expert community of players. To this end, this collaborativecompetitive game utilizes the Miniworld model which simulates three variables important for the sustainability of a society: Human population, economic production and the state of the environment. The resulting player interaction offers possibilities to collectively discover and validate potential scenarios for transitioning to a sustainable future, encouraging players to work together to balance the model output while also competing on individual objectives to be the individual winner of the game

Player retention in league of legends: A study using survival analysis

© 2018 ACM. Multi-player online esports games are designed for extended durations of play, requiring substantial experience to master. Furthermore, esports game revenues are increasingly driven by in-game purchases. For esports companies, the trends in players leaving their games therefore not only provide information about potential problems in the user experience, but also impacts revenue. Being able to predict when players are about to leave the game-churn prediction-is therefore an important solution for companies in the rapidly growing esports sector, as this allows them to take action to remedy churn problems. The objective of the work presented here is to understand the impact of specific behavioral characteristics on the likelihood of a player continuing to play the esports title League of Legends. Here, a solution to the problem is presented based on the application of survival analysis, using Mixed Effects Cox Regression, to predict player churn. Survival Analysis forms a useful approach for the churn prediction problem as it provides rates as well as an assessment of the characteristics of players who are at risk of leaving the game. Hazard rates are also presented for the leading indicators, with results showing that duration between matches played is a strong indicator of potential churn

Mysterious Murder - MCTS-driven murder mystery generation

© 2019 IEEE. We present an approach to procedurally generate the narrative of a simple murder mystery. As a basis for the simulation, we use a rule evaluation system inspired by Ceptre, which employs linear logic to resolve valid actions during each step of the simulation. We extend Ceptre's system with a concept of believable agents to make consecutive actions appear to have a causal connection so that players can comprehend the flow of events. The parts of the generated narratives are then presented to a player whose task it is to figure out who the murderer in this story could have been. Rather than aiming to replace highly authored narratives, this project generates puzzles, which may contain emerging arcs of a story as perceived by the player. While we found that even a simple rule set can create stories that are interesting to reason about, we expect that this type of system is flexible enough to create considerably more engaging stories if enough time is invested in authoring more complex rule sets

Personalized procedural map generation in games via evolutionary algorithms

In digital games, the map (sometimes referred to as the level) is the virtual environment that outlines the boundaries of play, aids in establishing rule systems, and supports the narrative. It also directly influences the challenges that a player will experience and the pace of gameplay, a property that has previously been linked to a player's enjoyment of a game [1]. In most industry leading games, creating maps is a lengthy manual process conducted by highly trained teams of designers. However, for many decades procedural content generation (PCG) techniques have posed as an alternative to provide players with a larger range of experiences than would normally be possible. In recent years, PCG has even been proposed as a means of tailoring game content to meet the preferences and skills of a specific player, in what has been termed Experience-driven PCG (EDPCG) [2]

Assessing user engagement with a fall prevention game as an unsupervised exercise program for older people

© 2018 ACM. Falling is, unfortunately, a leading cause of injury and death in the global elderly population. However, it has previously been shown that increased physical and cognitive activity can decrease the occurrence of falls in the elderly. This paper investigates the potential for a long-term, unsupervised fall prevention training tool in the form of the StepKinnection game, which was designed to exercise both reflex times and movement speed while also providing entertainment. Specifically, this game was used in a three month user study consisting of 10 participants over the age of 65. Adherence to the training program, enjoyment of the game, and ease of use of the game were investigated using a custom usability questionnaire, four established usability scales, heuristic evaluation of gameplay data, and semi-structured interviews. Results show that participants generally had positive attitudes towards the game, they felt that they would engage with this training program more than there current exercises, and that the game was easy to use without guidance or supervision beyond the initial set up support and instructions provided at the start of the experiment period

Combining skeletal tracking and virtual reality for game-based fall prevention training for the elderly

© 2018 IEEE. This paper provides a preliminary appraisal of combining commercial skeletal tracking and virtual reality technologies for the purposes of innovative gameplay interfaces in fall prevention exergames for the elderly. This work uses the previously published StepKinnection game, which used skeletal tracking with a flat screen monitor, as a primary point of comparison for the proposed combination of these interaction modalities. Here, a Microsoft Kinect is used to track the player's skeleton and represent it as an avatar in the virtual environment while the HTC Vive is used for head tracking and virtual reality visualization. Multiple avatar positioning modes are trialled and discussed via a small self-reflective study (with the authors as participants) to examine their ability to allow accurate stepping motions, maintain physical comfort, and encourage self-identification or empathy with the avatar. While this is just an initial study, it highlights promising opportunities for designing engaging step training games with this integrated interface but also highlights its limitations, especially in the context of an unsupervised exercise program of older people in independent living situations

Measuring player skill using dynamic difficulty adjustment

© 2018 ACM. Video games have a long history of use for educational and training purposes, as they provided increased motivation and learning for players. One of the limitations of using video games in this manner is, players still need to be tested outside of the game environment to test their learning outcomes. Traditionally, determining a player's skill level in a competitive game, requires players to compete directly with each other. Through the application of the Adaptive Training Framework, this work presents a novel method to determine the skill level of the player after each interaction with the video game. This is done by measuring the effort of a Dynamic Difficult Adjustment agent, without the need for direct competition between players. The experiments conducted in this research show that by measuring the players Heuristic Value Average, we can obtain the same ranking of players as state-of-the-art ranking systems, without the need for direct competition