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Identification of Player Types in Massively Multiplayer Online Games

TL;DR: This paper addresses the challenge of identification of player types in massively multiplayer online games (MMOGs) and demonstrates the approach using a PC.
Abstract: In this paper, we discuss an approach for identification of player types inmassively multiplayer online games (MMOGs). MMOGs provide fast growingonlinecommunities(Jarettet al.2003).Managingalarge-scalecommunityim-plies many challenges, such as identification of player types, social structures,and virtual economic mechanisms, etc. In this paper, we address the challengeonidentificationofplayertypes.AsafirststeptowarduseofrealMMOGdata,we demonstrate our approach using a PC
Citations
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01 Jan 2003
TL;DR: A conceptual software agent architecture for supporting users of mobile commerce services will be presented, including a peer-to-peer based collaborative filtering extension to support product and service recommendations and the proposed incremental classifier is shown to an order of magnitude faster than the other classifiers.
Abstract: Cyberspace plays an increasingly important role in people’s life due to its plentiful offering of services and information, e.g. the Word Wide Web, the Mobile Web and Online Games. However, the usability of cyberspace services is frequently reduced by its lack of customization according to individual needs and preferences.In this thesis we address the cyberspace customization issue by focusing on methods for user representation and prediction. Examples of cyberspace customization include delegation of user data and tasks to software agents, automatic pre-fetching, or pre-processing of service content based on predictions. The cyberspace service types primarily investigated are Mobile Commerce (e.g. news, finance and games) and Massively Multiplayer Online Games (MMOGs).First a conceptual software agent architecture for supporting users of mobile commerce services will be presented, including a peer-to-peer based collaborative filtering extension to support product and service recommendations.In order to examine the scalability of the proposed conceptual software agent architecture a simulator for MMOGs is developed. Due to their size and complexity, MMOGs can provide an estimated “upper bound” for the performance requirements of other cyberspace services using similar agent architectures.Prediction of cyberspace user behaviour is considered to be a classification problem, and because of the large and continuously changing nature of cyberspace services there is a need for scalable classifiers. This is handled by proposed classifiers that are incrementally trainable, support a large number of classes, and supports efficient decremental untraining of outdated classification knowledge, and are efficiently parallelized in order to scale well.Finally the incremental classifier is empirically compared with existing classifiers on: 1) general classification data sets, 2) user clickstreams from an actual web usage log, and 3) a synthetic game usage log from the developed MMOG simulator. The proposed incremental classifier is shown to an order of magnitude faster than the other classifiers, significantly more accurate than the naive bayes classifier on the selected data sets, and with insignificantly different accuracy from the other classifiers.The papers leading to this thesis have combined been cited more than 50 times in book, journal, magazine, conference, workshop, thesis, whitepaper and technical report publications at research events and universities in 20 countries. 2 of the papers have been applied in educational settings for university courses in Canada, Finland, France, Germany, Norway, Sweden and USA.

36 citations

Book ChapterDOI
01 Sep 2004
TL;DR: The experimental results given in this paper show that Hidden Markov Models have higher recognition performance than the previous approach, especially for classification of players of different types but having similar action frequencies.
Abstract: In this paper, we describe our work on classification of players in Massively Multiplayer Online Games using Hidden Markov Models based on player action sequences. In our previous work, we have discussed a classification approach using a variant of Memory Based Reasoning based on player action frequencies. That approach, however, does not exploit time structures hidden in action sequences of the players. The experimental results given in this paper show that Hidden Markov Models have higher recognition performance than our previous approach, especially for classification of players of different types but having similar action frequencies.

28 citations


Cites methods from "Identification of Player Types in M..."

  • ...2 This version of Zereal is different from the version that we used in our earlier reports in [1][3][8]....

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  • ...For performance comparisons, the game logs were also preprocessed by the feature selection algorithm originally proposed in [8] for being used by AMBR....

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Book ChapterDOI
01 Jan 2015
TL;DR: This chapter describes the processes to obtain user-generated gameplay data in situ using serious games for training—i.e., data tracing, cleaning, mining, and visualization and introduces a new Expertise Performance Index, based on string similarities that take into account the “course of actions” chosen by experts and compare that to those of the novices.
Abstract: Advances in technology have made it possible to trace players’ actions and behaviors (as user-generated data) within online serious gaming environments for performance measurement and improvement purposes. Instead of a Black box approach (such as pretest/posttest), we can approach serious games as a White box, assessing performance of play-learners by manipulating the performance variables directly. In this chapter, we describe the processes to obtain user-generated gameplay data in situ using serious games for training—i.e., data tracing, cleaning, mining, and visualization. We also examine ways to differentiate expert-novice performances in serious games, including behavior profiling. We introduce a new Expertise Performance Index, based on string similarities that take into account the “course of actions” chosen by experts and compare that to those of the novices. The Expertise Performance Index can be useful as a metric for serious games analytics because it can rank play-learners according to their competency levels in the serious games.

24 citations

Journal ArticleDOI
01 Jul 2007
TL;DR: A new framework for the process of visualizing online game-play data is presented, such as an event model and data acquisition infrastructure are derived and interface and implementation factors are explored, including in-situ game interface approaches.
Abstract: As massively multiplayer online games gain popularity, there has been a concomitant increase in their size and complexity, both technically and in terms of player usage. Quality assurance for such games takes up a major component of the development budget due to the painstaking work required to ensure the best possible gameplay. This article presents a new framework for the process of visualizing online game-play data. Major components such as an event model and data acquisition infrastructure are derived. Interface and implementation factors are also explored, including in-situ game interface approaches. We also expect that this framework can be modified for all genres of gaming that require an understanding of complex game-play.

14 citations

Dissertation
01 Jan 2004
TL;DR: Cyberspace plays an increasingly important role in people’s life due to its plentiful offering of services and information, e.g. the Word Wide Web, the Mobile Web and Online Games.
Abstract: Cyberspace plays an increasingly important role in people’s life due to its plentiful offering of services and information, e.g. the Word Wide Web, the Mobile Web and Online Games. However, the usa ...

10 citations

References
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Book
10 Jun 1997
TL;DR: One of the first practical guides to mining business data, Data Mining Techniques describes techniques for detecting customer behavior patterns useful in formulating marketing, sales, and customer support strategies.
Abstract: From the Publisher: Data Mining Techniques thoroughly acquaints you with the new generation of data mining tools and techniques and shows you how to use them to make better business decisions. One of the first practical guides to mining business data, it describes techniques for detecting customer behavior patterns useful in formulating marketing, sales, and customer support strategies. While database analysts will find more than enough technical information to satisfy their curiosity, technically savvy business and marketing managers will find the coverage eminently accessible. Here's your chance to learn all about how leading companies across North America are using data mining to beat the competition; how each tool works, and how to pick the right one for the job; seven powerful techniques - cluster detection, memory-based reasoning, market basket analysis, genetic algorithms, link analysis, decision trees, and neural nets, and how to prepare data sources for data mining, and how to evaluate and use the results you get. Data Mining Techniques shows you how to quickly and easily tap the gold mine of business solutions lying dormant in your information systems.

1,823 citations

Posted Content
TL;DR: In this article, growing artificial societies are modeled with cutting-edge computer simulation techniques and fundamental collective behaviors such as group formation, cultural transmission, combat, and trade are seen to emerge from the interaction of individual agents following a few simple rules.
Abstract: How do social structures and group behaviors arise from the interaction of individuals? Growing Artificial Societies approaches this question with cutting-edge computer simulation techniques. Fundamental collective behaviors such as group formation, cultural transmission, combat, and trade are seen to "emerge" from the interaction of individual agents following a few simple rules. In their program, named Sugarscape, Epstein and Axtell begin the development of a "bottom up" social science that is capturing the attention of researchers and commentators alike. The study is part of the 2050 Project, a joint venture of the Santa Fe Institute, the World Resources Institute, and the Brookings Institution. The project is an international effort to identify conditions for a sustainable global system in the next century and to design policies to help achieve such a system.

1,464 citations

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15 Oct 1990

1,129 citations

01 Jan 2002

20 citations