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Book ChapterDOI

User Type Identification in Virtual Worlds

01 Jan 2005-pp 79-88

TL;DR: This chapter presented an efiective approach for identification of user types in virtual worlds and AMBR, adopted as the classifier, could successfully identify the type of unknown user agents and could give higher performance with the item-based features.

AbstractIn this chapter we have presented an efiective approach for identification of user types in virtual worlds. Two types of input features were discussed, action-based features and item-based features. The former type uses the information on the frequency of each type of action that each user performed. The latter one uses the information on the frequency of each type of item that each user acquired. AMBR, adopted as the classifier, could successfully identify the type of unknown user agents. In addition, it could give higher performance with the item-based features. In future work, we plan to conduct experiments using agents with more complicated behaviors and to investigate use of order information in either action sequences or item sequences. Eventually, we will apply our findings to real user data.

Topics: User agent (52%), Metaverse (50%)

Summary (2 min read)

Introduction

  • A popular form of the virtual world is a massively multiplayer online game (MMOG).
  • In virtual worlds, four user types are typically identified by their characteristics, namely, "killer," "achiever," "explorer," and "socializer" [3] .
  • Socializer-type users are interested in relationships among users.
  • In the modeling phase, many types of user agents with different characteristics are modeled using the above MMOG simulator.
  • The first one is feature selection, namely, selection of input features from log data.

User Identification

  • User identification of a given user agent is performed merely from its log.
  • In their case, although type information is already available in the log, this information is not used.

Feature Selection

  • Two types of sequences, action sequences and item sequences, are generated by different algorithms.
  • Items sequences [7] are generated by the following algorithm: For monster items, if a user agent attacks a particular monster, add one monster item to the item sequence of that user agent.
  • For food, potion, and key items, if a user agent picks food, potion, or key, add one food, potion, or key item to the item sequence of that user agent, respectively.
  • The authors apply the following algorithm to action sequences to generate the input features for a classifier discussed in the next section.
  • Tables 3, 4 , and 5 show typical results of steps I, II, and III, respectively, for both action features and item features.

Classifier Selection

  • Here the authors adopt adaptive memory-based reasoning (AMBR) as the classifier in their experiments.
  • Given an unknown data to classify, MBR [9] performs majority voting of the labels (user types in their case) among the k nearest neighbors in the training data set, where the parameter k has to be decided by the user.
  • Figure 7 depicts the concept of AMBR with three types of data represented by circles, triangles, and squares.
  • To predict the type of unknown data represented by the cross, the procedure attempts to find the nearest neighbor (Fig. 7a ), but a tie occurs with two circles and two squares.

Experiments

  • Any classifier should be able to correctly identify unknown data not seen in the training data.
  • In the leave-one-out method, supposing that the total number of available data is M, first, data number 1 is used for testing and the other data are used for training the classifier of interest.
  • In the end, the averaged recognition rate for test data is computed, and is used to indicate the generalization ability of the classifier.
  • For experiments, log data were generated by running ten independent Zereal games with 500 simulation-time steps.

Conclusions

  • In this chapter the authors have presented an efiective approach for identification of user types in virtual worlds.
  • Two types of input features were discussed, action-based features and item-based features.
  • The former type uses the information on the frequency of each type of action that each user performed.
  • AMBR, adopted as the classifier, could successfully identify the type of unknown user agents.
  • Eventually, the authors will apply their findings to real virtual world data.

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79
User Type Identification in
Virtual Worlds
Ruck Thawonmas, Ji-Young Ho, and Yoshitaka Matsumoto
Intelligent Computer Entertainment Laboratory, Department of Human and Comput-
ational Science, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan. e-mail:
ruck@ci.ritsumei.ac.jp
Introduction
In this chapter, we discuss an approach for identification of user types in virtual
worlds. A popular form of the virtual world is a massively multiplayer online
game (MMOG). MMOGs provide fast-growing online communities [1], and
managing a large-scale virtual community implies many challenges, such as iden-
tification of user types, social structures, and virtual economic mechanisms [2]. In
this chapter, we address the challenge on identification of user types. It is very
important to grasp users’ needs and to satisfy them through furnishing appropri-
ate contents for each user or each specific group of users.
In virtual worlds, four user types are typically identified by their characteristics,
namely, “killer, “achiever, “explorer, and “socializer” [3]. Killer-type users
just want to kill other users and monsters with the tools provided. Achiever-type
users set their main goal to gather points or to raise levels while the explorer-
type user want to find out interesting things about the virtual world and then to
expose them. Socializer-type users are interested in relationships among users.
Following this categorization, a typical use of user-type identification results
can be depicted as in Fig. 1. In this figure, users are categorized into predefined
types based on appropriate selected features from the logs, and are provided
contents according to their favorites. Thereby, the users should enjoy the
virtual world more and hence stay longer. As a first step toward use of real virtual
world data, we demonstrate our approach using a PC cluster-based MMOG
simulator.
The work presented in this chapter is divided into two phases, namely, model-
ing and identification. In the modeling phase, many types of user agents with
different characteristics are modeled using the above MMOG simulator. By user
agents, we mean agents that imitate user characters in real MMOGs. The user
SAG_009.indd 79 3/3/06 5:45:26 PM
Agent-BasedModelingMeetsGamingSimulation(Post-ProceedingsoftheSessionConferenceoftheISAGA,
InternationalSimulationandGamingAssociation,2003),Series:SpringerSeriesonAgentBasedSocialSystems,
Vol.2Arai,Kiyoshi;Deguchi,Hiroshi;Matsui,Hiroyluki(Eds.),Springer(March2006),pp.79-88.

80 R. Thawonmas et al.
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agents reside in and migrate among multiple worlds, each world running on a PC
node. A world also accommodates monsters, representing nonplayer characters
in real MMOGs, that can kill (or be killed by) user agents.
In the identification phase, the task is to correctly identify the type of a given
user agent from its log. To perform this task, two technical issues are discussed.
The first one is feature selection, namely, selection of input features from log
data. The other one is classifier selection, namely, selection of a classifier for
identifying a given user agent to a particular type based on the selected input
features.
MMOG Simulator and Agent Modeling
The PC cluster-based MMOG simulator that we use is Zereal [4]. Zereal is a
multiagent simulation system [5]. It can simulate multiple worlds simultaneously,
running each world on a different PC node.
Figure 2 shows the architecture of Zereal. It is composed of one master node
and multiple world nodes. The master node collects the current status (world
model) of each world and forwards this information to a client computer for
visualization or data analysis. A world node simulates all objects such as user
agents and monster agents. Other objects include food items and potion items
for recovering stamina, and key items for opening a door in order to leave the
current world.
In the version of Zereal that we licensed from the Zereal developing team,
three types of user agents, namely, Killer, Markov Killer, and Plan Agent, are
provided. Each type has six common actions, namely, Walk, Attack, PickFood,
PickPotion, PickKey, and LeaveWorld, but each type is designed to have different
behavior described as follows:
Analysis
Log data
Selected
features
Feature selection
ResultsResults
Contents
Identi ficati on
Provision of contents for each specific group of users
Virtua l World
Fig. 1. Typical use of user-type identification results
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User Type Identification in Virtual Worlds 81
Killer puts the highest priority on killing monsters.
Markov Killer gets as many items as possible to be stronger. User agents of
this type also kill monsters, but attack monsters according to the corresponding
state-transitional probability.
Plan Agent finds a key and leaves the current world.
Killer, Markov Killer, and Plan Agent correspond to, to some extent, “killer,
“achiever, and “explorer, respectively, as described earlier.
To observe activities in the artificial societies, visualization tools are crucial
for jj (MASs). We have developed such a tool called ZerealViewer. Although
not yet fully functioned, a screen shot of the ZerealViewer when one world is
simulated is shown in Fig. 3.
Figure 4 shows a typical virtual world log sent to the client from the master
node for data analysis. The first and the second columns in the log indicate the
simulation time steps and the real clock time, respectively. The third column
shows the agent identifier numbers with the most upper digit(s) indexing the
current world node. The fourth column represents agent actions, and the fifth and
sixth columns show the coordinates in the world before and after such actions,
respectively. The last column gives information on the types of agents.
User Identification
User identification of a given user agent is performed merely from its log. In our
case, although type information is already available in the log, this information
is not used.
Feature Selection
Two types of sequences, action sequences and item sequences, are generated by
different algorithms. Action sequences [6] are generated from log data by extrac-
tion of action information. Items sequences [7] are generated by the following
algorithm:
Fig. 2. Zereal architecture
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82 R. Thawonmas et al.
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For monster items, if a user agent attacks a particular monster, add one monster
item to the item sequence of that user agent. If the user agent attacks the same
monster many times, only one monster item is added.
For food, potion, and key items, if a user agent picks food, potion, or key, add
one food, potion, or key item to the item sequence of that user agent,
respectively.
For door items, if a user agent leaves the world through a door, add one door
item to the item sequence of that user agent.
Figures 5 and 6 show the resulting action sequences and item sequences,
respectively. In addition, tables 1 and 2 show the relative frequencies of user agent
actions and user agent items, respectively. Because the tendencies of agent behav-
iors can be seen from the frequencies of action sequences and item sequences, it
is possible to identify user agents based on this kind of information.
We apply the following algorithm to action sequences to generate the input
features for a classifier discussed in the next section.
Fig. 3. Screen shot of ZerealViewer
Fig. 4. Typical virtual world log
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User Type Identification in Virtual Worlds 83
Fig. 5. Typical action sequences
Fig. 6. Typical item sequences
SAG_009.indd 83 3/3/06 5:45:29 PM

Citations
More filters

Journal ArticleDOI
TL;DR: This paper proposes a new player classification approach using action transition probability and Kullback Leibler entropy, which performed better than an existing approach based on action frequency and comparably to another existing approach using the Hidden Markov Model.
Abstract: Online game players are more satisfied with contents tailored to their preferences. Player classification is necessary for determining which classes players belong to. In this paper, we propose a new player classification approach using action transition probability and Kullback Leibler entropy. In experiments with two online game simulators, Zereal and Simac, our approach performed better than an existing approach based on action frequency and comparably to another existing approach using the Hidden Markov Model (HMM). Our approach takes into account both the frequency and order of player action. While HMM performance depends on its structure and initial parameters, our approach requires no parameter settings.

7 citations


Cites background or methods from "User Type Identification in Virtual..."

  • ...…Laboratory, Graduate School of Science and Engineering Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan E-mail: ruck@ci.ritsumei.ac.jp SAMSUNG ELECTRONICS CO.,LTD. [Received 00/00/00; accepted 00/00/00] Online game players are more satisfied with contents tailored to their preferences....

    [...]

  • ...In experiments with two online game simulators, Zereal and Simac, our approach performed better than an existing approach based on action frequency and comparably to another existing approach using the Hidden Markov Model (HMM)....

    [...]


Journal ArticleDOI
TL;DR: Results indicate that the Haar wavelet transform is effective in classification when the k-nearest neighbor classifier is used to classify players based on dynamic time warping distances between reconstructed sequences.
Abstract: Online game players’ action sequences, while important to understand their behavior, usually contain noise and/or redundancy, making them unnecessarily long. To acquire briefer sequences representative of players’ features, we apply the Haar wavelet transform to action sequences and reconstruct them from selected wavelet coefficients. Results indicate that this approach is effective in classificationwhen the k-nearest neighbor classifier is used to classify players based on dynamic time warping distances between reconstructed sequences.

4 citations


Cites background from "User Type Identification in Virtual..."

  • ...Results indicate that this approach is effective in classification when the k-nearest neighbor classifier is used to classify players based on dynamic time warping distances between reconstructed sequences....

    [...]


01 Aug 2011
TL;DR: An improved methodology for classifying players (identifying deviant players such as terrorists) through multivariate analysis of data from avatar characteristics and behaviors in massive multiplayer online games (MMOGs) is developed.
Abstract: : The purpose of our research is to develop an improved methodology for classifying players (identifying deviant players such as terrorists) through multivariate analysis of data from avatar characteristics and behaviors in massive multiplayer online games (MMOGs). To build our classification models, we developed three significant enhancements to the standard Generalized Regression Neural Networks (GRNN) modeling method. The first enhancement is a feature selection technique based on GRNNs, allowing us to tailor our feature set to be best modeled by GRNNs. The second enhancement is a hybrid GRNN which allows each feature to be modeled by a GRNN tailored to its data type. The third enhancement is a spread estimation technique for large data sets that is faster than exhaustive searches, yet more accurate than a standard heuristic. We applied our new techniques to a set of data from the MMOG, Everquest II, to identify deviant players ('gold farmers'). The identification of gold farmers is similar to labeling terrorists in that the ratio of gold farmer to standard player is extremely small, and the in-game behaviors for a gold farmer have detectable differences from a standard player. Our results were promising given the difficulty of the classification process, primarily the extremely unbalanced data set with a small number of observations from the class of interest. As a screening tool our method identifies a significantly reduced set of avatars and associated players with a much improved probability of containing a number of players displaying deviant behaviors. With further efforts at improving computing efficiencies to allow inclusion of additional features and observations with our framework, we expect even better results.

2 citations


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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.

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"User Type Identification in Virtual..." refers background in this paper

  • ...Given an unknown data to classify, MBR [9] performs majority voting of the labels (user types in our case) among the k nearest neighbors in the training data set, where the parameter k has to be decided by the user....

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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


"User Type Identification in Virtual..." refers methods in this paper

  • ...MMOG Simulator and Agent Modeling The PC cluster-based MMOG simulator that we use is Zereal [4]....

    [...]


01 Jan 2003
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

14 citations


"User Type Identification in Virtual..." refers background in this paper

  • ...Action sequences [6] are generated from log data by extraction of action information....

    [...]