People love to capture their memoriesand experiences to create personal his-tories and track events,1 as evidencedby the success of myspace.com andyoutube.com. However, users must
manually construct such collections, and orga-nizing and labeling the contents is time consum-ing. A more traditional method for recording
memories is a diary, but noteveryone has the daily habit ofwriting in a journal. Yet thesetypes of information storage canaugment our memory2 and helpus predict future events and re-
call old ones. We thus aimed to create a systemthat could automate and complement such diary-generation procedures.
Our goal was to summarize a given user’s dailylife with a cartoon-style diary based on informationcollected from mobile devices such as smartphones. Our system, AniDiary (Anywhere Diary),addresses two main problems of typical diary sys-tems—the huge number of events originating fromthe real-life log and the awkward presentation ofthe output. Using modular Bayesian networks,AniDiary can detect and visualize landmarks (rel-evant or novel events) and transform numerouslogs into user-friendly cartoon images. The car-toons provide a good starting point for fine-grainedsearches of detailed information. For example,users can link each cartoon to rich media (photosor videos) that offer more details, reducing the
search space and letting users easily recall thelinked details.
This application of mobile devices promises toprovide a new way for people to manage theirpersonal information.
AniDiaryExpanding on others’ research, we organized
our system similarly to how human memory isstructured (see the “Related Work on Life Log-ging” sidebar).3 We used several Bayesian net-works designed by experts to find memorableevents in a modular manner. Bayesian networksare one of the most efficient ways of inferring sit-uations given a certain amount of uncertain orpartial information. After AniDiary logs and pre-processes events, it selects the most memorableones and converts them into cartoons by select-ing a set of cartoon image components from adatabase and composing them into a cartoon.Figure 1 shows the overall procedure.
LoggingNumerous sources are available for logging
information, and only some of them requireattaching additional devices to the phone. Forexample, ContextPhone is context-logging soft-ware for Nokia 60 smart phones,4 and its sourceis available to the public. It collects informationon a wide range of topics by
• logging photographs taken, music downloads,
AniDiary (Anywhere Diary) uses Bayesian networks to automatically detect landmark events and summarize a user’s daily life in a cartoon-style diary.
M O B I L E C O M P U T I N G
Sung-Bae Cho, Kyung-Joong Kim,Keum Sung Hwang, and In-Ji SongYonsei University
short-message-service use, multimedia-messaging service use, and Bluetoothuse;
• monitoring the phone’s battery level; • storing call logs; and• recording applications in use.
However, because ContextPhone isbased on the Symbian operating system,we also developed a logging module forthe Windows mobile environment. (In2006, 51 percent of mobile device oper-ating systems used Symbian and only 17percent used Windows. However, by2010, the Diffusion group forecasts that22 percent of mobile devices will use Sym-bian and 29 percent will use Windows.5)
Our logging system runs on WindowsCE with a small GPS receiver attached tothe device. The system continuously re-cords the user’s latitude and longitude andlets users easily access call logs and theaddress book. It also stores SMS texts, fre-quently compressing them according tothe device manufacturer’s standards (todo this, we have to contact the manufac-turer). We also modified the code for aphoto viewer and an MP3 player andadded it to our system to log usage infor-mation. We can easily gather a photo’screation time and other low-level infor-mation from the photo’s header. The sys-tem retrieves weather information fromthe Korean Meteorological Administra-tion (www.kma.go.kr), and it samples theGPS and battery level once per second.
PreprocessingThis stage employs standard statistical
analysis to extract significant information.Because raw information isn’t meaning-ful, we use statistical variations to detectinformative situations.
For example, we infer a user’s currentposition from the GPS value of a pair oflongitude and latitude coordinates. (We“infer” because a device can sometimeslose its GPS signal in a building or a shad-owed area of an urban environment, orwhen placed in a pocket or bag. Cell-basedlocation positioning is available, but itsaccuracy is worse than with the GPS.)Using the GPS value, a Web service (http://maps.naver.com) identifies the nearestbuilding. We then transform the raw infor-mation into semantic labels using storedinformation about the relationship be-tween the GPS value and the semanticlabel (the building or street name). Thestored information might include seman-tic labels such as “my home,” “my office,”or “my friend’s home.” The user can man-ually input this information using Ani-Diary’s map-based visualization.
To determine discrete information usingSMS text, call logs, photos, and MP3 selec-tions, we extract patterns using simple sta-tistical techniques, such as determining theaverage, maximum, and minimum valuesor the frequency over the time domain.
Detecting memory landmarksIn this stage, user feedback and learn-
ing procedures can reduce the overhead ofmanually constructing a detection model.
The idea of landmarks stems fromhuman memory research, which hasshown that the brain stores related eventstogether as “episodes” and uses land-mark events to point to each episode. Theproblem is identifying the landmarkevents and labeling them so that you canuse them for future indexing. When peo-ple try to remember things, the query isoften unclear and we sometimes missrelated events, so we must deal withuncertainty and missing variables. ABayesian network can address such prob-lems by providing a robust inferencebased on probability theory.
SMILE (Structural Modeling, Inference,and Learning Engine) is a Bayesian net-work library for mobile devices (http://genie.sis.pitt.edu). Although it supportsways to implement Bayesian networkinference in mobile devices easily, it can’thandle the inference that comes from ex-tremely large Bayesian networks. Becauseour focus is on daily life, the Bayesiannetwork could be large enough to causereal-time errors. So, we structured theBayesian network in a modular ratherthan monolithic fashion.
A daily diary’s domain includes manyactivities, and we can’t incorporate themall into a single model. So, we proposeusing an ensemble of multiple Bayesiannetworks specialized for each activity.Each model is manually designed by
JULY–SEPTEMBER 2007 PERVASIVEcomputing 67
Logging
Landmark detectionmodel learning
Preprocessing
Landmarkdetection
model database
Landmarkdetection
User logdatabase
Story ontology
Storygeneration
Cartoongeneration
Cartoonimage database
Interaction
Cartoondiary
Context
Figure 1. AniDiary’s general architecture.
experts, based on the independence ofrandom variables. Each model has itsown input, intermediate, and output vari-ables, some of which it can share withother models. We manually determine thecausal relationships between the variablesusing the expert knowledge, and the sys-tem determines the conditional probabil-ities using the noisy-or method. Virtualevidence techniques let us input othermodels’ probabilistic outputs.
Increasing the number of stages in theinference requires more computationalresources, so we limited the number ofstages to two. In the first stage, AniDiaryinputs evidences to each Bayesian net-work and calculates each network’s out-put. In the second stage, it uses the firststage’s output as input evidences for theother Bayesian networks.
In figure 2, the dotted line indicates avirtual link as well as the stream of thesecond stage of inference processing.Parentheses indicate the number ofBayesian networks. We used 39 Bayesiannetworks, broken down into four kinds:
• Circumstantial/situational: space, cli-mate, time, device, and group; and
• Event: anniversaries and other events.
The 39 networks contain 638 nodes,623 links, and 4,205 conditional proba-bility values (CPVs). Merging these into asingle model results in 462 nodes (weremoved duplicate nodes). The modularBayesian networks make an inference 39times for average of 16.6 nodes and 107.8CPVs. Meanwhile, the single model makesone inference for 469 nodes and 4,869CPVs. Usually, the mobile version ofBayesian networks might not deal withsuch large Bayesian networks owing tomemory problems.
Story and cartoon generationFor story generation, a template-based
method with an ontology is a goodchoice, but there are several difficultissues to consider.
The easiest way to present landmark
68 PERVASIVEcomputing www.computer.org/pervasive
M O B I L E C O M P U T I N G
Akey organizational principle of human memory is episodic storage
and retrieval. Our brains group related events as episodes and use
landmark events to recall these episodes. Finding such landmark events
can also recall related items. Similarly, Bayesian networks can detect
landmark events from the data stored in schedulers.
Eric Horvitz, Susan Dumais, and Paul Koch attempted to reorga-
nize personal information storage in desktop PCs in terms of an
episodic style of memory.1 They built a single Bayesian network
using data from a desktop environment for landmark detection.
We’ve expanded this idea by designing an ensemble of Bayesian
networks for landmark detection.
Our research also borrows from the Massachusetts Institute of
Technology’s research. The MIT Reality Mining group has developed
a serendipity service, which cues informational, face-to-face interac-
tions between nearby users who don’t know each other but prob-
ably should. Their service uses the ContextPhone software,2 and
they’ve been collaborating with the MIT Common Sense Reasoning
group to generate diaries automatically. Because the research is still in
the early stages, although the Reality Mining group has made avail-
able its visualization tool for a collected log, it has yet to produce any
concrete results. However, their work shows a new way of generating
more interpretable high-level diaries using common sense. (Basic de-
tails about common sense knowledge appear elsewhere.3) Our work
is based on this ontology and can be expanded to more general mod-
els using such a common sense corpus.
Other related work is the comic diary system Yasuyuki Sumi and
his colleagues designed to summarize conference tours in a cartoon-
style form.4 They based their system on explicit user input including
schedule information.
Nathan Eagle has also tried to develop a diary system based on log
information collected from cellular phones.5 This system showed raw
information directly on a GUI, which made it difficult to intuitively
understand the big picture of a given day. Nokia’s Lifeblog service
gives users a way to store and manage photographs, multimedia,
and short-message-service messages chronologically (see www.
nokia.com/lifeblog). However, it doesn’t use any abstraction or sum-
marization methods.
REFERENCES
1. E. Horvitz, S. Dumais, and P. Koch, “Learning Predictive Models ofMemory Landmarks,” Proc. 26th Ann. Meeting Cognitive Science Soc.,Lawrence Erlbaum Associates, 2004, pp. 583–588.
2. N. Eagle and A. Pentland, “Social Serendipity: Mobilizing SocialSoftware,” IEEE Pervasive Computing, vol. 4, no. 2, 2004, pp. 28–34.
3. P. Singh, B. Barry, and H. Liu, “Teaching Machines about Everyday Life,”BT Technology J., vol. 22, no. 4, 2004, pp. 227–240.
4. Y. Sumi et al., “ComicDiary: Representing Individual Experiences in aComic Style,” Proc. UbiComp, Springer, 2002, pp. 16–32.
5. N. Eagle, “Machine Perception and Learning of Complex SocialSystems,” PhD thesis, Program in Media Arts and Sciences, Massachu-setts Inst. of Technology, 2005.
Related Work on Life Logging
detection results is chronologically, butthis can lead to a boring or redundantstory. Reorganizing the landmarks oftenbetter captures the event. The detectedlandmarks are connected if cause-and-effect relationships exist between them.Such a connection results in a number ofgraphs with different landmark aver-ages. AniDiary then sequentially presentsthe highest landmarks from each graph.
A single cartoon cut combines fiveimages, overlaying text, subcharacters,main characters, the subbackground,and the main background (see figure 3).Professional artists prepare a set ofimages for each of the five image types.For example, our system contains 253main background images. We then fuseone of these images with one of our 21subbackground images, which representvarious weather conditions. So the totalnumber of possible background imagesis 253 � (21 + 1) = 5,566. The systemcontains 356 main characters (178 Asianand 178 Western characters). It includes69 exaggerated images and nine ani-mated images, and 26 subcharactersconsisting of four types (man, woman,Asian, Western). The total number ofimages composed of the two charactertypes is 178 � (26 + 1) � 2 = 9,612.Approximately 53 million cartoons arepossible.
AniDiary determines the number ofcartoons on the basis of the thresholdvalue for landmark detection. The largerthe value, the fewer the cartoons, becausefewer landmarks exceed the threshold.
Experimental resultsWe tested the landmark-detection
module’s performance using artificialdata generated from predefined rules.First, we used data generated for onlyone day, then for 30 days. We also askedusers to evaluate our cartoon images,
and eventually we used real log data toevaluate system performance.
A preliminary testWe tested the proposed landmark-
reasoning model using an artificial sce-nario (see figure 4a) to measure the per-formance of the manually designed mod-ular Bayesian networks. The modelincorporates prior knowledge about usersand their living patterns. From the 16-hour scenario, we generated log contextsfor 24 hours, and then we tested the data.
JULY–SEPTEMBER 2007 PERVASIVEcomputing 69
Log context
Landmark
Bayesian networks
Place-activity
(19)
Emotional/conditional
(13)
Circumstantial/situational
(5)
Event(2)
1st stage 2nd stage
Landmark
Firstpreprocessing
module
Secondpreprocessing
module
BNj
Preprocessing of landmark evidence
(a) (b)
BNi
Figure 2. (a) The two-stage inference process for the cooperation of modular Bayesian networks. (b) Bayesian networks modularized for efficiency.
Main background
Subbackground
Main character
Subcharacter
Text
Figure 3. The composition of image components for a single cartoon cut.
70 PERVASIVEcomputing www.computer.org/pervasive
M O B I L E C O M P U T I N G
home
park
school
restaurant
downtown
coffeeshop
home
9 a.m.
10 a.m.
11 a.m.
12 p.m.
1 p.m.
2 p.m.
3 p.m.
4 p.m.
5 p.m.
6 p.m.
7 p.m.
8 p.m.
9 p.m.
10 p.m.
11 p.m.
12 a.m.
moving
moving
Activity
lecturebuilding
student hall
lecture building
Scenario
Today is a school day
Morning lecture
Simple lunchWalk through a park
Take photo of spring flowers
Meet friendsGo to a restaurant
Go to a coffee shopChat
Funny and joyful day
Target landmark
Going-outpreparation
Walking
Taking a photopleasantly
Eating out
Having tea
Joyful
Attendinga lecture
Attendinga lecture
Eating
Sleeping
Sleeping(a)
Joyful
Photo (scenery)
Taking a photo pleasantly
Walking
Having tea
Eating out
Eating (Western style)
Eating
Preparing to go out
Taking a shower
Sleeping
Targ
et la
ndm
ark
Goin
g-ou
t pr
epar
atio
n
Wal
king
Taki
ng a
pho
topl
easa
ntly
Eatin
g ou
t
Tea
Joyf
ul
Lect
ure
Lect
ure
Eatin
g
Slee
ping
Slee
ping
(b)
4 a.m
.
5 a.m
.
6 a.m
.
7 a.m
.
8 a.m
.
9 a.m
.
10 a.
m.
11 a.
m.
12 p.
m.
1 p.m
.
2 p.m
.
3 p.m
.
4 p.m
.
5 p.m
.
6 p.m
.
7 p.m
.
8 p.m
.
9 p.m
.
10 p.
m.
11 p.
m.
12 p.
m.
1 a.m
.
2 a.m
.
3 a.m
.
Figure 4. The probability change of each landmark node inferred in Bayesian networks. The change verifies that AniDiary makes a proper inference for the scenario: (a) A summary of a normal day using an undergraduate student’s mobile device. (b) The observation of the probabilities of 11 target landmarks. The denoted time is from 4 a.m. to 3 a.m. the following day.
Figure 4b shows the inference resultsbased on recorded landmark probabilityincrements. For example, the “preparingto go out” and “taking a shower” land-marks occurred from 7 to 9 a.m., “eat-ing” from 12 to 1 p.m. and from 5 to 7p.m., “walking” from 1 to 2 p.m., “tak-ing a photo pleasantly” from 2 to 3 p.m.,and “eating out” and “eating (Westernstyle)” from 5 to 7 p.m. The inferenceresults (or probability transitions) indi-cate that the modular model producesappropriate probability given the sce-nario’s artificial data.
Performance evaluationon long-term data
To produce a more realistic evaluation,we collected data for 30 days, grouping thesituations into two conditions—usual/unusual and idle/busy. We generated artifi-cial high-level contexts because controllingthe raw data directly was difficult. Forexample, we created a context called “a lotof phone calls,” which we used in place ofthe phone-call log data. We randomly se-lected two landmarks from each of the 30days—one from the morning and the otherfrom the afternoon.
Table 1 shows the results. We excludedthe landmarks related to the default place“home” and the less significant landmarksfrom the main landmark set. The false-positive error of the “usual/idle” conditionwas high and precision was low, becausethe “usual”’ condition included manyduplicate places and landmarks. The false-positive error of the “usual/busy” condi-tion was low because the conditionincluded a relatively high number of land-
marks that occurred frequently at the reg-ular time such as routine SMS texts, fre-quent calling, and active movements. Theoverall recall rate was as low as 75 per-cent. This resulted from a lack of tuningor from hard-to-detect landmarks.
In table 1, two target objects (withfew redundancies) were selected. Theunusual/busy data were composed ofone unusual landmark and one busylandmark.
Image generation testTo test the image-generation capability,
we used a scenario in which the user wentto school (listening to MP3 music), stud-ied (with some difficulty), ate, walked,enjoyed a concert, and went out drinking.Each event had three to five possible car-toons. Figure 5a shows the cartoons de-scribing the conditions. We didn’t use thesame number of cartoons for each condi-tion because each had a different numberof variations. Sometimes we needed morecartoons to show different actions for onegiven event. We then generated four stories(see figure 5b) and evaluated their levelsof diversity and consistency in terms ofevent representation.
Sixteen graduate students evaluatedthe generated cartoons by answeringfour questions using a five-point scale:
Q1: These are cartoon imagesdescribing specific conditions. Pleaseevaluate the correctness of eachimage given the condition (5—verycorrect, 4—correct, 3—average, 2—incorrect, 1—very incorrect).
Q2: Please evaluate the diversity ofimages given conditions (5—very
Q3: The four stories are composedof six cartoons, and they representthe daily life of a female student. Theschedule of the day is as follows:Going to school while listening toMP3 music, studying with some dif-ficulty, eating, walking, enjoying aconcert, and going out drinking.Please rank the four stories on thebasis of their measure of correctness(Give the most accurate story a 5and the least accurate story a 1.)
Q4: Please order the four stories onthe basis of the measure of fun.
Figure 6a shows the evaluation of pre-sentational power (average 2.96) and thepossibility of diverse representation of thesame events (average 3.54). By selectingone cartoon randomly for each event,there are 4,500 variations (3 � 4 � 3 � 5� 5 � 5), and we randomly chose four sto-ries (figure 6b). The correlation betweenquestions 2 and 4 is 3.0 (positive value),indicating that funny cartoons will likelybe diverse.
Evaluation using a real-life logA female university student used her
mobile phone with the logging softwareto evaluate the landmark detectionmodel for 27 days. The number of sam-pled input contexts depended on suc-cessfully collecting GPS signals. Thethreshold for the landmark selection was66 percent. We determined “correct”and “partially correct” data on the basisof the user’s daily report and visualizedanalysis of the user’s GPS log. Table 2shows the results.
JULY–SEPTEMBER 2007 PERVASIVEcomputing 71
TABLE 1Experimental results with synthetic data.
True False Falsepositive positive negative
No. of No. of target error error error Class days landmarks rate (%) rate (%) rate (%) Precision Recall
Usual/idle 30 60 46 14 14 0.767 0.767
Unusual/idle 30 58 43 10 15 0.811 0.741
Usual/busy 30 55 41 2 14 0.953 0.745
Unusual/busy 30 60 46 8 14 0.852 0.767
Total 120 233 176 34 57 0.838 0.755
72 PERVASIVEcomputing www.computer.org/pervasive
M O B I L E C O M P U T I N G
(a)
(b)
Going to school with MP3 music
Studying with some difficulty
Eating
Walking
Enjoying a concert
Drinking
Story 1
Story 2
Story 3
Story 4
Figure 5. We used landmark examples to compose various cartoon stories: (a) the various cartoons describing the conditions and(b) the four stories we generated.
The results show that the correct ratio(RHIT) is only 34.1 percent, owing to thedifficulty of interpreting the real situa-tion given a limited daily report. How-ever, if the detected landmarks reason-ably relate to the daily report’s events,we refer to them as “partially correct”and view them as sufficient for generat-ing a meaningful diary. The partially cor-
rect ratio (RHIT�) is 89.4 percent. Figure 7 presents three comic diaries
generated from the real log data. Figure7a depicts the user on 27 February 2006:she went to school late (image 1) andsent an angry SMS message because ofa traffic jam (image 2). After studying,she ate lunch (images 3–5). After walk-ing around the campus (image 6), she
went home (images 7 and 8). The lunchscene is duplicated because she visitedthe restaurant a second time to buy cof-fee. Our system couldn’t classify thedetails of her behavior in this instance—it couldn’t distinguish between eatinglunch and buying coffee.
Figure 7b shows the user on 5 March2006 in her hometown (image 1). After
JULY–SEPTEMBER 2007 PERVASIVEcomputing 73
(b)(a)
Scor
e
Scor
e
Going t
o sch
ool
Studyin
gEa
ting
Walking
Conce
rt
Drinkin
g
5
4
3
2
1
0
Story 1
Story 2
Story 3
Story 4
5
4
3
2
1
0
Q1
Q2
Q3
Q4
Figure 6. Evaluation of (a) the cartoons for each event in terms of their presentational power and (b) the stories and their represen-tation of the same events. The black marks indicate the standard deviation.
TABLE 2Accuracy of the real-world data set.
Day NCon* NLM
† NLM’‡ NHIT** NHIT’†† NERR
‡‡ RHIT (%)** RHIT’ (%)†† RERR (%)‡‡
24 Feb 116 72 13 3 10 0 23.1 100.0 0.0
27 Feb 167 49 15 4 11 0 26.7 100.0 0.0
28 Feb 64 50 8 3 4 1 37.5 87.5 12.5
2 Mar 202 128 18 8 10 0 44.4 100.0 0.0
4 Mar 102 53 7 1 5 1 14.3 85.7 14.3
6 Mar 86 56 12 5 3 4 41.7 66.7 33.3
8 Mar 114 92 12 3 7 2 25.0 83.3 16.7
9 Mar 103 45 7 4 2 1 57.1 85.7 14.3
15 Mar 128 76 13 4 9 0 30.8 100.0 0.0
17 Mar 46 45 8 3 3 2 37.5 75.0 25.0
21 Mar 67 40 10 4 4 2 40.0 80.0 20.0
1195 706 123 42 68 13 34.1 89.4 10.6
* NCon: No. of input data samples† NLM: No. of detected landmarks‡ NLM’: No. of detected landmarks excluding duplicated and low probability landmarks (below threshold)** NHIT and RHIT: No. and ratio of exactly correct landmarks, respectively †† NHIT’ and RHIT’: No. and ratio of approximately correct landmarks, respectively ‡‡ NERR and RERR: No. and ratio of wrong landmarks, respectively
walking around for a while (image 2),she took a bus downtown (image 3).Because the bus ride was long, the car-toon represents the ride with a personwho is sad. However, our location-mapping system couldn’t provide richenough information to accurately repre-sent her hometown, which is in the coun-
try, so it failed to generate successful out-put for the rest of the day.
Figure 7c shows the user on 9 March2006. She took part in a university con-cert as a staff member. Image 5 correctlyrepresents the main event. Image 4 illus-trates how busy she was before the con-cert. Most of the images correctly repre-
sent the day’s main events, even thoughthere are some awkward images due tothe limited data.
Our long-term goals are to eval-uate the system using real logscollected over a long timeperiod from real subjects and
to apply our techniques to online com-munities of personal, virtual space suchas Cyworld, MySpace, and orkut. In par-ticular, Cyworld (of SK Communicationsin South Korea) has attracted manyyoung people because it lets them easilybuy items to decorate their blogs (90 per-cent of South Koreans in their 20s haveregistered with this site). We also hope todevelop a more sophisticated learningalgorithm for landmark detection for per-sonalized detection models.
ACKNOWLEDGMENTSThis research was supported in part by the Sam-sung Advanced Institute of Technology and MIC(Korea) under ITRC IITA-2005-(C1090-0501-0019).
2. S. Vemuri, and W. Bender, “Next-Genera-tion Personal Memory Aids,” BT Technol-ogy J., vol. 22, no. 4, 2004, pp. 125–138.
3. E. Horvitz, S. Dumais, and P. Koch, “Learn-ing Predictive Models of Memory Land-marks,” 26th Ann. Meeting Cognitive Sci-ence Soc., Lawrence Erlbaum Associates,2004, pp. 583–588.
4. M. Raento et al., “ContextPhone: A Proto-type Platform for Context-Aware MobileApplications,” IEEE Pervasive Computing,vol. 4, no. 2, 2005, pp. 51–59.
5. L. Allen, “Advanced Mobile Operating Sys-tems: Comparative Analyses & Forecast,”The Diffusion Group, 2005.
74 PERVASIVEcomputing www.computer.org/pervasive
M O B I L E C O M P U T I N G
(a)
(b)
(c)
1 2 3 4
5 6 7 8
1 2 3 4
5 6 7 8
1 2 3 4
5 6 7 8
Figure 7. Comic diaries generated fromreal log data for (a) 27 February, (b) 5March, and (c) 9 March.
JULY–SEPTEMBER 2007 PERVASIVEcomputing 75
the AUTHORSSung-Bae Cho is a professorin Yonsei University’s Depart-ment of Computer Science.His research interests includeneural networks, pattern rec-ognition, intelligent man-machine interfaces, evolu-tionary computation, andartificial life. He received his
PhD in computer science from KAIST (the KoreaAdvanced Institute of Science and Technology).He’s a senior member of the IEEE and a memberof the Korea Information Science Society, ACM,IEEE Computational Intelligence Society, IEEEComputer Society, and IEEE Systems, Man, andCybernetics Society. Contact him at the Dept. ofComputer Science, Yonsei Univ., 134 Shinchon-dong, Sudaemoon-ku, Seoul 120-749, Korea;[email protected].
Kyung-Joong Kim is a re-searcher in the Yonsei Univer-sity’s Department of Com-puter Science. His researchinterests include evolutionaryneural networks, pattern rec-ognition, evolutionary com-putation, and artificial life. Hereceived his PhD in computer
science from Yonsei University. He’s a member ofthe IEEE and ACM. Contact him at the Dept. ofComputer Science, Yonsei Univ., 134 Shinchon-dong, Sudaemoon-ku, Seoul 120-749, Korea;[email protected].
Keum-Sung Hwang is a re-searcher at the SoftcomputingLaboratory and a doctoral stu-dent in computer science atYonsei University. His researchinterests include Bayesiannetworks and evolutionaryalgorithms for context-awarecomputing and intelligent
agents. He received his MSc in computer sciencefrom Yonsei University. He’s a student member of the IEEE. Contact him at the Dept. of Com-puter Science, Yonsei Univ., 134 Shinchon-dong,Sudaemoon-ku, Seoul 120-749, Korea; [email protected].
In-Ji Song is a master’s stu-dent in computer science atYonsei University. His researchinterests include intelligentagents and adaptive userinterfaces. He received his BSin computer science fromYonsei University. Contacthim at the Dept. of Com-
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