Feature selection for capturing the experience of fun

Several approaches for constructing metrics to capture player experience have been presented previously. In this paper, we propose a generic methodology based on feature selection and preference machine learning for constructing such metric models of the degree to which a player enjoys a given game. For that purpose, previous and new survey experiments on computer and physical interactive games are presented. Given effective data collection a set of numerical features is extracted from a player’s interaction with the game and its physiological state. Then feature selection algorithms are employed together with a function approximator based on artificial neural networks to construct feature sets and function that model the players’ notion of ‘fun’ for the game under investigation. Performance of the model is evaluated by the degree to which the preferences predicted by the model match those ‘fun’ (entertainment) preferences expressed by the subjects. The results show that effective models can be constructed using the proposed approach. The limitations and the use of the methodology as an effective adaptive mechanism to entertainment augmentation are discussed.This work was supported in part by the Danish Research Agency, Ministry of Science, Technology and Innovation (project no: 274-05-0511).peer-reviewe

Understanding Co-evolution in Large Multi-relational Social Networks

Understanding dynamics of evolution in large social networks is an important problem. In this paper, we characterize evolution in large multi-relational social networks. The proliferation of online media such as Twitter, Facebook, Orkut and MMORPGs\footnote{Massively Multi-player Online Role Playing Games} have created social networking data at an unprecedented scale. Sony's Everquest 2 is one such example. We used game multi-relational networks to reveal the dynamics of evolution in a multi-relational setting by macroscopic study of the game network. Macroscopic analysis involves fragmenting the network into smaller portions for studying the dynamics within these sub-networks, referred to as `communities'. From an evolutionary perspective of multi-relational network analysis, we have made the following contributions. Specifically, we formulated and analyzed various metrics to capture evolutionary properties of networks. We find that co-evolution rates in trust based `communities' are approximately $60\%$ higher than the trade based `communities'. We also find that the trust and trade connections within the `communities' reduce as their size increases. Finally, we study the interrelation between the dynamics of trade and trust within `communities' and find interesting results about the precursor relationship between the trade and the trust dynamics within the `communities'

Hacker Combat: A Competitive Sport from Programmatic Dueling & Cyberwarfare

The history of humanhood has included competitive activities of many different forms. Sports have offered many benefits beyond that of entertainment. At the time of this article, there exists not a competitive ecosystem for cyber security beyond that of conventional capture the flag competitions, and the like. This paper introduces a competitive framework with a foundation on computer science, and hacking. This proposed competitive landscape encompasses the ideas underlying information security, software engineering, and cyber warfare. We also demonstrate the opportunity to rank, score, & categorize actionable skill levels into tiers of capability. Physiological metrics are analyzed from participants during gameplay. These analyses provide support regarding the intricacies required for competitive play, and analysis of play. We use these intricacies to build a case for an organized competitive ecosystem. Using previous player behavior from gameplay, we also demonstrate the generation of an artificial agent purposed with gameplay at a competitive level

An Elo-Based Approach to Model Team Players and Predict the Outcome of Games

Sports data analytics has become a popular research area in recent years, with the advent of different ways to capture information about a game or a player. Different statistical metrics have been created to quantify the performance of a player/team. A popular application of sport data anaytics is to generate a rating system for all the team/players involved in a tournament. The resulting rating system can be used to predict the outcome of future games, assess player performances, or come up with a tournament brackets. A popular rating system is the Elo rating system. It started as a rating system for chess tournaments. It’s known for its simple yet elegant way to assign a rating to a particular individual. Over the last decade, several variations of the original Elo rating system have come into existence, collectively know as Elo-based rating systems. This has been applied in a variety of sports like baseball, basketball, football, etc. In this thesis, an Elo-based approach is employed to model an individual basketball player strength based on the plus-minus score of the player. The plus-minus score is a powerful metric because it quantifies the contribution of a player like good defense, setting up screens, or sledging the opposite team, which are not reflected by metrics that are primarilybased on points. Then, the individual player ratings are combined to obtain a team rating, Team rating are compared pairwise to obtain the probability of a win by each of the teams during a matchup. This method not only predicts wins/losses, but offers more information than the Elo rating system as ratings are assigned to each individual player instead of just considering teams. This information includes for example, the effect of mid-season transfers or the impact of injuries to team strengths; these items are overlooked by the standard Elo algorithm. The performance of the proposed Elo-based rating system is compared to that of the standard Elo rating system for basketball by using sythetic data. The rating systems are also compared by running them over real-life data from past NBA seasons

The Measurement and Evaluation of Professional League of Legends Teams for Optimal Strategy

With ever improving streaming technologies and accessibility to video games, it comes asno surprise that competitive gaming or eSports have blown up in recent time. League ofLegends, former gaming startup Riot Games' sole intellectual property, has the title mostpopular eSport in the world with a thriving competitive scene and international competitionthat rivals traditional sports leagues such as the MLB, the NBA and the NFL [Sta, 2013].With the high stakes involved in the burgeoning eSports industry, it is imperative that theseorganizations develop methods that can dierentiate players based on their skill throughtheir in-game performance metrics and determine potential acquisitions. Additionally, wewant to leverage the data within Riot Games' databases on how the general playerbaseapproaches the game to determine what how in game performance metrics change as playerskill increases. The end goal of this analysis is to create a method to gauge team performanceand assess weak links in strategy

Seeking Excellence: Improving Objectivity in Player Analysis in Professional Basketball

This thesis details the creation and testing of an original statistical metric for analyzing individual basketball players in the National Basketball Association (NBA) by both their commonly measured statistics and their so-called “intangibles.” By using existing methods as both guides and a caution against potential shortcomings, an inclusive statistic with multiple layers of data can be built to best reflect an individual player’s overall value to his team. This metric will be adjusted to account for the differences across multiple eras of NBA play and the levels of talent with which a player played in order to avoid penalizing a player for the unique aspects of his career