Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games

Esports has emerged as a popular genre for players as well as spectators, supporting a global entertainment industry. Esports analytics has evolved to address the requirement for data-driven feedback, and is focused on cyber-athlete evaluation, strategy and prediction. Towards the latter, previous work has used match data from a variety of player ranks from hobbyist to professional players. However, professional players have been shown to behave differently than lower ranked players. Given the comparatively limited supply of professional data, a key question is thus whether mixed-rank match datasets can be used to create data-driven models which predict winners in professional matches and provide a simple in-game statistic for viewers and broadcasters. Here we show that, although there is a slightly reduced accuracy, mixed-rank datasets can be used to predict the outcome of professional matches, with suitably optimized configurations

Gamification design for motivating and measuring modal shift

Cities across the world attempt to minimise the negative environmental and wellbeing effects of increasing traffic volume and density. To this end, an increasing number of cities have taken to games and gamified applications to motivate mobility behaviours with less adverse effects. Being a novel approach predominantly deployed on online platforms, a major challenge of this approach is designing systems to generate valid in-the-wild mobility behaviour data to assess their effectiveness. Drawing on experiences from an on-going development project of a gamified application targeting tourist behaviour in York (UK) city centre, this paper discusses how a mobile gamified application driving sustainable behaviours can be designed to quantify its impact. It provides recommendations on how gamification design can allow for a measurable output on the levels of modal shift gained through in game promotion of alternative modes of transport

Exploration and Skill Acquisition in a Major Online Game

Using data from a major commercial online game, Destiny, we track the development of player skill across time. From over 20,000 player record we identify 3475 players who have played on 50 or more days. Our focus is on how variability in elements of play affect subsequent skill development. After validating the persistent influence of differences in initial performance between players, we test how practice spacing, social play, play mode variability and a direct measure of game-world exploration affect learning rate. These latter two factors do not affect learning rate. Players who space their practice more learn faster, in line with our expectations, whereas players who coordinate more with other players learn slower, which contradicts our initial hypothesis. We conclude that not all forms of practice variety expedite skill acquisition. Online game telemetry is a rich domain for exploring theories of optimal skill acquisition

Clyde: A deep reinforcement learning DOOM playing agent

In this paper we present the use of deep reinforcement learn-ing techniques in the context of playing partially observablemulti-agent 3D games. These techniques have traditionallybeen applied to fully observable 2D environments, or navigation tasks in 3D environments. We show the performanceof Clyde in comparison to other competitors within the con-text of the ViZDOOM competition that saw 9 bots competeagainst each other in DOOM death matches. Clyde managedto achieve 3rd place in the ViZDOOM competition held at theIEEE Conference on Computational Intelligence and Games2016. Clyde performed very well considering its relative sim-plicity and the fact that we deliberately avoided a high levelof customisation to keep the algorithm generic

Using Association Rule Mining to Predict Opponent Deck Content in Android: Netrunner

As part of their design, card games often include information that is hidden from opponents and represents a strategic advantage if discovered. A player that can discover this information will be able to alter their strategy based on the nature of that information, and therefore become a more competent opponent. In this paper, we employ association rule-mining techniques for predicting item multisets, and show them to be effective in predicting the content of Netrunner decks. We then apply different modifications based on heuristic knowledge of the Netrunner game, and show the effectiveness of techniques which consider this knowledge during rule generation and prediction

Multi-Agent Credit Assignment in Stochastic Resource Management Games

Multi-Agent Systems (MAS) are a form of distributed intelligence, where multiple autonomous agents act in a common environment. Numerous complex, real world systems have been successfully optimised using Multi-Agent Reinforcement Learning (MARL) in conjunction with the MAS framework. In MARL agents learn by maximising a scalar reward signal from the environment, and thus the design of the reward function directly affects the policies learned. In this work, we address the issue of appropriate multi-agent credit assignment in stochastic resource management games. We propose two new Stochastic Games to serve as testbeds for MARL research into resource management problems: the Tragic Commons Domain and the Shepherd Problem Domain. Our empirical work evaluates the performance of two commonly used reward shaping techniques: Potential-Based Reward Shaping and difference rewards. Experimental results demonstrate that systems using appropriate reward shaping techniques for multi-agent credit assignment can achieve near optimal performance in stochastic resource management games, outperforming systems learning using unshaped local or global evaluations. We also present the first empirical investigations into the effect of expressing the same heuristic knowledge in state- or action-based formats, therefore developing insights into the design of multi-agent potential functions that will inform future work

A strategic roadmap for BM change for the video-games industry

The global video games industry has experienced and exponential growth in terms of socioeconomic impact during the last 50 years. Surprisingly, little academic interest is directed towards the industry, particularly in the context of BM Change. As a technologically intensive creative industry, developing studios and publishers experience substantial internal and external forces to identify, and sustain, their competitive advantage. To achieve that, managers are called to systematically explore and exploit, alternative BMs that are compatible with the company’s strategy. We build on empirical analysis of the video-games industry to construct a Toolkit that i) will help practitioners and academics to describe the industrial ecosystem of BMs more accurately, and ii) use it a strategic roadmap for managers to navigate through alternatives for entrepreneurial and growth purposes