We propose a visualization approach for analyzing players' action behaviors. The proposed approach consists of two visualization techniques: classical multidimensional scaling (CMDS) and KeyGraph. CMDS is for discovering clusters of players who behave similarly. KeyGraph is for interpreting action behaviors of players in a cluster of interest. In order to reduce the dimension of matrices used in computation of the CMDS input, we exploit a time-series reduction technique recently proposed by us. Our visualization approach is evaluated using log of an online game where three-player types according to Bartle's taxonomy are found, that is, achievers, explorers, and socializers.
The market size of online games continues to
experience surging growth [
Most work in the literature focuses on visualization
of player trails or time series of visited locations for examining the distance
over time among the members of a social group [
This research, however, focuses on visualizing player
behaviors based on their actions. According to Bartle's taxonomy [
In this paper, we propose an approach for visualizing
players' action behaviors using classical multidimensional scaling (CMDS)
[
In this
section, we describe CMDS, KeyGraph, log format, and visualization metrics. As
with most other tools for information visualization [
CMDS is a
prevailing technique for mapping pair-wise relationships to coordinates and has
been applied to several areas such as statistics, psychology, sociology,
political sciences, and marketing [
In our research, the
Here, we
describe how action sequences are numerically coded into time-series matrices
for computation of DTW distances. Let
Consider, for example, the set of action symbols
The DTW
distance between time-series matrices
Consider, for example, the set of symbols
Time-series matrices
Derivation of dynamic time
warping distance between
KeyGraph is a
visualization tool for discovery of relations among text-based data. Its
underlying concept is based on a building construction metaphor. As shown in
[
Three major components of KeyGraph are as follows.
Foundations: subgraphs of highly associated
and frequent terms representing basic concepts in the data. Roofs: terms highly associated with
foundations. Columns: associations between foundations and
roofs used for extracting keywords or main concepts in the data.
Associations between terms are defined as the
co-occurrence among them in same sentences, and keywords are the terms in
either foundations or roofs that are connected to strong columns. Under
KeyGraph representation, solid lines and their touching black nodes depict
foundations, dotted lines depict columns, red nodes depict roofs excluding
those in the foundations, and double circles depict keywords. We use a tool
called Polaris [
Figure
KeyGraph applied to the abstract of this paper.
Player action
sequences in our work are sequences of action symbols extracted from game log,
of an online game discussed in Section
According to a recent work in [
Achievement consisting of three subcomponents,
that is, advancement, mechanics, and competition. Social consisting of three subcomponents, that
is, socializing, relationship, and teamwork. Immersion consisting of four subcomponents,
that is, discovery, role-playing, customization, and escapism.
The achievement, social, and immersion categories correspond to Bartle's achievers,
socializers, and explorers, respectively, although the above ten motivations
overlap among player types.
In our work, we focus in particular on
advancement described in [ socializing described in [ discovery described in [
This is because
we anticipate that they should be identifiable using our action sequences and
KeyGraphs. Below, we verify this anticipation with simplified data sets and
their KeyGraphs, which serve as our visualization metrics for facilitating
interpretation of KeyGraph results in Section
Let us consider a set of action symbols
Figure
(a) KeyGraphs for achievers, (b) socializers, and (c) explorers generated from the given sample data sets, where m (interaction to a mission master), c (chat), and r (interaction to a remote object) are the keywords in (a), (b), and (c), respectively.
Our technique
for obtaining compact sequences representing major player behaviors is based on
Haar wavelet transform [
In the wavelet transform concept, decomposition
involves obtaining wavelet coefficients from a sequence of interest.
Reconstruction involves recovering the original sequence from obtained
coefficients. Henceforth, it is assumed that the length
Example of Haar wavelet transform.
Resolution | Averages | Coefficients |
---|---|---|
(6, 8, 2, 7, 6, 5, 4, 3) | — | |
(7.0, 4.5, 5.5, 3.5) | (−1.0, −2.5, 0.5, 0.5) | |
(5.75, 4.5) | (1.25, 1) | |
(5.125) | (0.625) |
Reconstruction of a given sequence from its Haar
wavelet coefficients and averages is done as follows:
Now, we describe our procedure for reducing the length
of the time-series matrix of an action sequence of interest. For explanation,
we use action sequence
(i) Derive time-series matrix
Time-series matrix
(ii) Decompose each row in
(iii) Reconstruct each row in
Reconstruction of each row in
(iv) Reconstruct
Reconstructed
(v) Reduce the size of
Reduced
Note that the DTW distance between the reduced
time-series matrices
We obtained
player log from the online game The ICE [
A screen shot of The ICE.
Map and the positions of NPCs and monsters.
Item delivery where the PC must deliver an
item from the mission issuing NPC to a specified NPC. Item trade where the PC must trade with NPCs
to increase the amount of money initially provided by the mission issuing NPC. Monster extermination where the PC must help
the mission issuing NPC by exterminating monsters.
Actions available in The ICE are summarized in Tables
Action list of The ICE.
Action | Symbol |
---|---|
Attack with a snow ball | (c.f., top half part of Table |
Chat | c |
Walk | w |
Trade | t |
Talk | (c.f., bottom half of Table |
Pick up potion | p |
Use potion | u |
Dead | d |
Warp | x |
List of additional symbols related to actions Attack and Talk.
Symbol | Description |
---|---|
A | Attack to monster 1 |
B | Attack to monster 2 |
C | Attack to monster 3 |
D | Attack to boss monster |
E | Attack to other game objects |
G | Talk to the item shop |
H–M | Talk to NPCs 1–6 |
N–V | Talk to NPCs 7–15 |
W | Talk to NPC 16 |
A group of 20 players, on average 20 years of age, participated in this evaluation. These players consisted of third-year and fourth-year computer science undergraduate students who were familiar with online-games but had no experience in playing The ICE. After a brief introduction to the game, they were asked to arbitrarily play it, starting from Town 1. In addition to these 20 players, labeled p1–p20, three game masters, JOJO, Justice, KURO, also participated in the event. In the rest of our evaluation, the symbol w was removed from the log because it was frequently present in all players' action sequences and thus bared no information.
Table
Mean and variance of the data lengths before and after applying the time-series reduction technique.
MEAV | VAR | |
---|---|---|
Before | 262.7 | 7688.6 |
After | 25.5 | 13.1 |
Figure
MDS result for all data.
MDS result for data after exclusion of the outliers.
Figure
KeyGraph of cluster 1, where rD (attacks the boss monster residing in the most remote map from the initial map) is one of the keywords.
They moved away from town 1 and fought
monsters 2 and 3. They also went to the end of the map and
fought the boss monster. They were not active in pursuing missions.
The above summary is based on our interpretation of this KeyGraph as follows. First, it can be seen that the foundation of this KeyGraph is mainly composed of warp and attack (monsters 2 and 3) nodes. Next, the symbol rD is a keyword indicating that these players went far away to the end of the map and fought the boss monster there. In addition, because there is only one NPC symbol J in the keywords, these players were not active in receiving missions, from NPCs, and in pursuing them.
Consequently, it can be stated that the players in cluster 1 like to explore the world map and that these players have no interest in pursuing missions and only fight monsters when they find them. This type of players fits Bartle's explorer.
Figure
KeyGraph of cluster 2, where L and R (talk to mission NPCs) are among the keywords.
They mainly moved within town 1. They also fought monsters 2 and 3. They received a lot of missions.
The above summary is based on our interpretation of this KeyGraph as follows. First, it can be seen that besides nodes related to fighting (B, C, E, u, p), nodes of NPCs residing in town 1 (I, J, K) are included in the foundation of this KeyGraph. This indicates that these players were mainly in town 1. In addition, the keywords include symbols L and R which denote NPCs who are involved in several missions.
As a result, it can be stated that the players in cluster 2 are aggressive in pursuing missions, especially those completable within or not far away from town 1. This type of players fits Bartle's achiever.
Figure
KeyGraph of cluster 3, where c (chat) is one of the keywords on the foundation.
They chatted a lot. They mainly fought monsters 2 and 3. They also fought the boss monster.
The above summary is based on our interpretation of this KeyGraph as follows. First, the foundation of this KeyGraph includes the symbol c, not seen in the foundation or keywords of the previous two clusters. This indicates that these players chatted a lot among each other. Next, the symbol rD is a keyword showing that the players also fought the boss monster. We have confirmed through directly investigating the log that a group of three players (p5, p7, p8) and another group of four players (p16, p17, p18, p19) frequently chatted among their group members and that each group went together to the end of the map to fight the boss monster.
From the above interpretation, it can be stated that the players in this cluster like to communicate with others via chats. This type of players fits Bartle's socializers.
We give here the computational complexity of the techniques used in our approach.
Understanding
the player behaviors is an important issue in improving the service quality of
online games. We have proposed a visualization approach that first locates
clusters of players who have similar action behaviors using CMDS and then
interprets such behaviors of a cluster of interest using KeyGraph. To increase
the efficiency in computation of the CMDS input, we have described the use of
the time-series reduction technique proposed recently by us in [
Our future work is to apply the proposed approach to log from commercial online games and to examine if Bartle's player types can be found. It might also be interesting to investigate log formats whose information can be used for automatically identifying other types of Nick Yee's play motivations.
This work was supported in part by Grant-in-Aid for Scientific Research (C) no. 20500146 from the Japan Society for Promotion of Science. The authors would like to thank the anonymous reviewers for their invaluable comments.