User Type Identification in Virtual Worlds

TLDR: 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.

Abstract: In 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.

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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
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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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• 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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Fig. 5. Typical action sequences
Fig. 6. Typical item sequences
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