Group items tagged

Abstract: "A key problem for crowd - sourced systems is motivating contributions from participants and ensuring the quality of these contributions. Games have been suggested as a motivational approach but there are concerns about data quality , particularly when the data are to be used for scientific research . To address these concerns, w e compare the quality of data obtained from two citizen science games, one a "gamified" version of a species classificatio n task and one a fantasy game that used the classification task only as a way to advance in the game play. Surprisingly, though we did observe cheating in the fantasy game, data quality from the two games was not significantly different . As well, the quali ty of data from short - time contributors was at a usable level. These findings suggest that games can be a useful way to motivate contributions to citizen science projects. "

Abstract: "We define data games as games where gameplay and/or game content is based on real-world data external to the game, and where gameplay supports the exploration of and learning from this data. This concept is discussed in rela- tion to open data, procedural content generation and serious games, and research challenges are outlined. To illustrate the concept, we present six prototype games and content generators of our own making. We also present a tentative taxonomy of actual and potential data games, and situate the described games within this taxonomy."

"A data game is a game that allows the player(s) to explore data that is derived from outside the game, by transforming the data into something that can be played with. In other words, games as a form of interactive data visualisation."

From the abstract: "Collective Artificial Intelligence (CAI) simulates human intelligence from data contributed by many humans, mined for inter-related patterns. This thesis applies CAI to social role-playing, introducing an end-to-end process for compositing recorded performances from thousands of humans, and simulating open-ended interaction from this data. The CAI process combines crowdsourcing, pattern discovery, and case-based planning. Content creation is crowdsourced by recording role-players online. Browser-based tools allow non-experts to annotate data, organizing content into a hierarchical narrative structure. Patterns discovered from data power a novel system combining plan recognition with case-based planning. The combination of this process and structure produces a new medium, which exploits a massive corpus to realize characters who interact and converse with humans. This medium enables new experiences in videogames, and new classes of training simulations, therapeutic applications, and social robots. .... As a proof of concept, a CAI system has been evaluated by recording over 10,000 performances in The Restaurant Game, automating an AI-controlled waitress who interacts in the world, and converses with a human via text or speech. Quantitative results demonstrate CAI supports significantly open-ended interaction with humans, while focus groups reveal factors for improving engagement."

Abstract: "HCI scholars have been among those attracted to the study of online, computer-supported gaming. "Big Data" approaches, which analyze electronic traces left by game play, are an increasingly popular way to study it. This paper identifies basic epistemological problems in some such approaches, focusing on those that implicitly depend on the assumption that game play is fundamentally the same as other social activity. The paper explains why this and related assumptions are questionable, and why these Big Data approaches cannot establish their validity on their own. The paper then reports some results of a preliminary ethnographic study of Massive Multiplayer Online Games (MMOGs), in order to illustrate a way that ethnography can provide an initial purchase on how the underlying similarity/dissimilarity issue can be studied. It concludes by explaining how methodological triangulation, involving a dialectical discourse between ethnography, on the one hand, and Big Data and similar approaches, on the other, may be able to place Game Studies on a firmer epistemological foundation. It is the attempt to achieve such significant objectives, in particular to justify a foundational critique of a major new development in Game Studies, and to do so in a single paper, that justify inclusion of the paper in alt.chi. "

Abstract: "This paper proposes a new framework for automated analysis of game-play metrics for aiding game designers in nding out the critical aspects of the game caused by factors like design modications, change in playing style, etc. The core of the algorithm measures similarity between spatial distribution of user generated in-game events and automatically ranks them in order of importance. The feasibility of the method is demonstrated on a data set collected from a modern, multiplayer First Person Shooter, together with application examples of its use. The proposed framework can be used to accompany traditional testing tools and make the game design process more efficient."

Abstract: "With the advent of the increasingly participatory Internet and the growing power of the crowd, "Serious Games" have proven to be a fertile approach for gathering task-specific natural language data at very low cost. In this paper we outline a game we call Doodling, based on the sketch-andconvey metaphor used in the popular board game Pictionary ®2, with the goal of generating useful natural language data. We explore whether such a paradigm can be successfully extended for conveying more complex syntactic and semantic constructs than the words or short phrases typically used in the board game. Through a series of user experiments, we show that this is indeed the case, and that valuable parallel language data may be produced as a byproduct. In addition, we explore extensions to this paradigm along two axes - going online (vs. face-to-face) and going crosslingual. The results in each of the sets of experiments confirm the potential of Doodling game to generate data in large quantities and across languages, and thus provide a new means of developing data sets and technologies for resource- poor languages."

"This paper describes design issues surrounding "Data Games," small-scale, short web-based games that generate data for analysis in mathematics or statistics classes. The games are embedded in a data analysis environment. We discuss design for the games themselves as well as some curriculum and assessment issues."

Abstract: "This paper presents data analysis from a course on Software Engineeri ng in an effort to identify metric s and techniques that would allow instructor to act proactively and identify patterns of low engagement and inefficient peer collaboration . Over the last two terms, 106 students in their second year of studies formed 20 groups and worked collaboratively to develop video games. Throughout the lab, students have to use a variety of tools for managing and developing their projects, such as software version control , static analysis tools , wikis, mailing lists, etc . The students are also supported by weekly meet ings with teaching assistants and instructors regarding group progress, code quality, and management issues. Through these meeting s and their interactions with the software tools, students leave a detailed trace of data related to their individual engageme nt and their collaboration behavior in their groups . The paper provides discussion on the different source of data that can be monitored , and present preliminary results on how the s e data can be used to analyze students' activity."

"Electronic Arts CTO Rajat Taneja on big data's growing role in the video game world."

Abstract: "Herein is proposed the creation of an Application Program- ming Interface (API) for the Unity 3 and 4 video game de- velopment engine that not only reads behavioral measures from traditional video game input devices (such as if there has been an increase in mouse movements and clicks) but also takes into account physiological measures from biomet- ric devices (such as an increase in respiratory rate). The API parses these inputs based on study results that correlated player performance and engagement with physio- logical signs across several dierent game genres. Through the use of several rudimentary machine learning algorithms, raw physiological data is transformed into data relevant to a developer, including player engagement. The results of these calculations allow a game designer to have powerful tools for detecting when players experience certain emotions, and al- low for the design of aective games. Furthermore, the API also exposes the raw data to de- velopers wishing to propose and utilize their own learning algorithms, to allow for a rich development environment for developers of all skill levels. These development tools will enrich the game experience for the player, as well as prepare designers for the use of the next wave of non-traditional in- put hardware. This report serves to illustrate the current status of the API. A brief overview of the signicance of galvonic skin re- sponse (GSR), heart rate (HR), and respiratory rate (RR) in detecting player performance and engagement will be fol- lowed by a discussion of the API itself and the design choices therein."

Abstract :"Immigrants entering the European Community face a range of challenges in adapting to and understanding the culture of their host nation. Failure to address these challenges can lead to isolation and difficulties integrating into the society of the host country, leading to fragmented communities and a range of social issues . As part of a comprehensive suit e of services for immigrants , the European - funded M obile Assistance for Social Inclusion and Empowerment of Immigrants with Persuasive Learning Technologies and Social Network Services (MASELTOV) project seeks to provide both practical tools and le arning services via mobile devices, providing a readily usable resource for immigrants . In this workshop paper, the game - based learning aspect of the MASELTOV project is introduced, with the rationale behind its design presented. In doing so, the b enefits and implications of mobile platforms and emergent dat a capture techniques for game - based learning are discussed, as are methods for putting engaging gameplay at the forefront of the experience whilst relying on rich data capture and analysis to pro vide an effective learning solution. Through comparison to several other projects, a number of recommendations are put forward for games deployed in contexts similar to that of MASELTOV: a focus on establishing a significant audience with which to conduct ethical research into efficacy, the need for robust pedagogical frameworks suited to the learning context, and the evolution of methods for data capture and analysis of player activity."

Abstract: "ARTigo is a Game with a Purpose platform whose goal is to collect tags for artworks, which can be used for retrieval. The ARTigo gam es form a tagging ecosystem, where the games cooperate in order to gather a wide range of information. Description games create initial tags, in order to enable other games to be played and collect semantically shallow surface tags. Dissemination games sp read existing data to other artworks or languages, and do not create new information. Diversification games feed on data produced by other games and guide the players to entering more specific tags. Finally, integration games create higher - order tags with deep semantic meanings, describing emotions for example. All these different types of tags have different semantic depths, and are present in different quantities. Together, they create a pool of complementary tags accurately describing artworks, which can be processed for data mining."

Abstract: "A recent trend within computational intelligence and games research is to investigate how to affect video game players' in-game experience by designing and/or modifying aspects of game content. Analysing the relationship between game content, player behaviour and self-reported affective states constitutes an important step towards understanding game experience and constructing effective game adaptation mechanisms. This papers reports on further refinement of a method to understand this relationship by analysing data collected from players, building models that predict player experience and analysing what features of game and player data predict player affect best. We analyse data from players playing 780 pairs of short game sessions of the platform game Super Mario Bros, investigate the impact of the session size and what part of the level that has the major affect on player experience. Several types of features are explored, including item frequencies and patterns extracted through frequent sequence mining."

From the abstract: "A serious game for construction planning and scheduling education must provide an authentic environment for gameplay. To achieve this requirement, the game environment must be created from CAD drawings of a real project. The game engine must have components for providing timely scaffolding and support to the user. Storing the vast amount of data for a real project requires data structures optimized for fast rendering at the same time easily accessing and manipulating building elements and element data. This paper discusses a game engine developed for creating construction planning and scheduling educational games. The game engine is designed from scratch for performance and flexibility. It includes a component for directly importing data from a Revit model for building the game environment, interfaces Microsoft Project for scheduling, includes a feedback module, and a scoring system for measuring user performance."

Abstract: "Player metrics are an invaluable resource for game designers and QA analysts who wish to understand players, monitor and improve game play, and test design hypotheses. Usually such metrics are collected in a straightforward manner by passively recording players; however, such an approach has several potential drawbacks. First, passive recording might fail to record metrics which correspond to an infrequent player behavior. Secondly, passive recording can be a costly, laborious, and memory intensive process, even with the aid of tools. In this paper, we explore the potential for an active approach to player metric collection which strives to collect data more efficiently, and thus with less cost. We use an online, iterative approach which models the relationship between player metrics and in-game situations probabilistically using a Markov Decision Process (MDP) and solves it for the best game configurations to run. To analyze the benefits and limitations of this approach, we implemented a system, called GAMELAB, for recording player metrics in Second Life."

Abstract: "A number of changes are occurring in the field of computer game development: persistent online games, digital distribution platforms, social and mobile games, and the emer- gence of new business models have pushed game development to put heavier emphasis on the live operation of games. Artificial intelligence has long been an important part of game development practices. The forces of change in the industry present an opportunity for Game AI to have new and profound impact on game development practices. Specifically, Game AI agents should act as "producers" responsible for managing a long-running set of live games, their player communities, and real-world context. We characterize a confluence of four major forces at play in the games industry today, together producing a wealth of data that opens unique research opportunities and challenges for Game AI as producers. We enumerate 12 new research areas spawned by these forces and steps toward how they can be addressed by data-driven Game AI Producers"

"In this work we present a methodology especially designed to facilitate usability testing for serious games, taking into account the specific needs of such applications and resulting in a systematically produced list of suggested improvements from large amounts of recorded gameplay data. This methodology was applied to a case study for a medical educational game, MasterMed, intended to improve patients' medication knowledge. We present the results from this methodology applied to MasterMed and a summary of the central lessons learned that are likely useful for researchers who aim to tune and improve their own serious games before releasing them for the general public."

"The U.S. government has hired a California-based company to hack into video game consoles, such as Xbox 360 and PlayStation 3, to watch criminals, especially child predators, and learn how to collect evidence against them."

From the abstract: "Our goal is to create a better understanding on how to design encounters that affect interest levels in linear FPS games. Specifically, how exactly a player's interest levels can be raised intentionally. To accomplish this, we developed a method for measuring and comparing player interest levels based on electroencephalogram (EEG) data measured using a "Neurosky Mindset" unit, which is a commercially available EEG device. We measured player EEG data for the first 4 levels of the FPS game "Call of Duty: Modern Warfare 2" and calculated player interest levels for the entire length of gameplay. By referring to recorded gameplay videos, we were able to associate each increase in interest levels for each player to a specific point in game. From this data, we found and isolated points in each level where most players showed a rise in interest levels, and discovered that certain patterns existed between these situations. These findings led to another study where we further analyzed these situations and found out what factors caused most players to show a rise in interest levels. We were able to divide these factors into 6 different categories called Triggers: "Anticipation", "Concentration", "Surprise", "Frustration", "Overwhelm" and "Fear". As these triggers represent mutually exclusive elements that affect a player's interest levels, we found that in most situations where many test subjects showed a rise in interest levels, more than one trigger is present, a phenomenon we call Stacking. While our study is still ongoing, we believe that by using these triggers as guidelines, game designers will be able to intentionally plan and control the player's interest level for FPS games with a certain level of guarantee"