Table Of ContentStudies in Computational Intelligence 423
Editor-in-Chief
Prof.JanuszKacprzyk
SystemsResearchInstitute
PolishAcademyofSciences
ul.Newelska6
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E-mail:[email protected]
Forfurthervolumes:
http://www.springer.com/series/7092
Yukio Ohsawa and Akinori Abe (Eds.)
Advances in Chance Discovery
Extended Selection from International
Workshops
ABC
Editors
Prof.Dr.YukioOhsawa AkinoriAbe
TheUniversityofTokyo FacultyofLetters
Bunkyo-ku ChibaUniversity
Japan Chiba
Japan
ISSN1860-949X e-ISSN1860-9503
ISBN978-3-642-30113-1 e-ISBN978-3-642-30114-8
DOI10.1007/978-3-642-30114-8
SpringerHeidelbergNewYorkDordrechtLondon
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Preface
Yukio Ohsawa and Akinori Abe
The beginning of year 2000, we started to study methods for discovering
events/situations that significantly affect decision making. The triggers to this
movement and its aftershocks occurred with the initiator Ohsawa’s movements
from/to schools in engineering to business sciences since around in April 1999.
When he was studying methods for visualizing data on earthquakes with his
original KeyGraph(cid:2) (See chapters in [Ohsawa and McBurney, 2003] for some
use cases), where high-risk faults of earthquakes were shown by red nodes rep-
resentingrarelyquakingfaults affected by frequently quakingactive faults visu-
alized by black nodes, the business students who work at daytime who learned
rare but significant events may get highlighted by KeyGraph suggested such a
graph may be useful for aiding decision making in business.
As a result, they started to collaborate with companies to visualize data
on businesses, to detect noteworthy events which might be rare but useful for
planning new actions and decisions. Such events or situations are sometimes
called opportunities, and other times crisis. With realizing this kind of suc-
cesses (also see Fig. 1, they started series of workshops and special sessions
on what they called Chance Discovery (IECON2000, KES 2000, AAAI2001,
ECAI2004, etc) — second editor Abe joined these activities, and now is lead-
ing the world-wide network of researchers on chance discovery. Through these
active efforts, the participants’ vague dream converged to a unique definition
of a chance, i.e., an event which has a significant impact on human’s deci-
sion making. Thus a chance provides an uncertain opportunity/risk for human.
And, chance discovery is the discovery of a chance, emphasized in contrast to
discovery by chance. Because the essential aspect of a chance is that it can
be the seed of new and significant benefits/loss for human, the sense of hu-
man(s) about benefit/loss is a significant factor for chance discovery as well
as machine’s power to analyze and visualizing data for aiding humans’ talent.
VI Preface
Fig. 1. A successful case in the classical chance discovery: The clusters of frequently
ordered items are bridged via a rare item, from which textile marketers concluded
to propose a new selling scenario: Make a jacket for business people with the new
corduroy,towhich they can change from suitsto go out for dinnerafter workingtime
[Ohsawa and Usui, 2005].
As wellasworkshopsonchancediscovery,they publishedbooksandissuesfrom
journals, characterizing chance discovery with studies on:
(1) Humanfactorsincognitions,communications,andthoughtsforcatchingup
with a chance and understanding its significance for decision making,
(2) Process for externalizing candidates of chances and discovering chances on
objective evidences — reinforcing (1) by data in other words, and
(3) Method and tools mining/visualizing data that aid in chance discovery —
accelerating and sustaining the process of (2) in other words.
In other words, the design of interaction among human(s), computers, and
their environment came to the core interests in studies for realizing chance dis-
covery. The papers contributed to chance discovery so far have been by re-
searchersfromvariousdomainssuchasartificialintelligence,creativitysupport,
economics, business administration, risk management, operation research, lin-
guistics, mathematics, physics, psychology etc showing approaches relevant to
classes (1), (2), and (3).
For the present book, we selected authors of outstanding papers which were
presented on chance discovery in relevant workshops, conferences, and sym-
posia — mainly from workshops in the International Joint Conference on Arti-
ficialIntelligence (IJCAI 2011),IEEEInternationalConference onData Mining
(ICDM2011),IEEEInternationalConferenceonSystems,Man,andCybernetics
Preface VII
(SMC2010). Collaboratorswho utilized or extended studies in chance discovery
forcontributingtootherbutrelevantconferences,suchasofmulti-agentsandju-
ristic communications (AAMAS2011, JURISN2011) were also invited. As guest
editors, we are happy to find three chapters for class (1), five for (2), and seven
for (3) in this book. Readers will find new visions for the decision making of
human, from this harmonious combination of contributions.
In the first part, we show contributions to the aspect of Cognition and Com-
municationtowardChanceDiscovery.Abe’sintroductionoftheconceptcuration
in Chapter 1 encourages us to discuss the meaning of chance discovery from a
different aspect coming from the exhibition of artwork in a gallery. Borrowing
ideasfromcasesofdatacurationandcurationofartwork,hesuggestsadirection
to curation for aiding chance discovery, and leads to the concept to the role of
communicationinchancediscovery.InChapter2,byBardoneandMagnani,the
bullshitting phenomenathatisasignificantkindofchancefaking,ishighlighted
and explained with respect to one’s carelessness about frames, evidences, and
simplicity that one should stay aware of for taking real advantage of notewor-
thy chances.The messagessuggestedin this paper areinstructive andusefulfor
people who seek chances — we learn from this paper that we need evidential
data and also concrete interests in explaining the data in the form of a simple
logic under a suitable context, whether or not data mining tools are provided.
Terai and Miwa links chance discovery to their original studies on insight in
Chapter 3, that means to release humans from impasse, so that noticing the
value of a new event gets enabled. They argue that in insight problem solv-
ing tasks, a prepared mind precedes the “aha” experience, as in the process to
chance discovery. In order to preparing mind for chance discoveries, they point
out interdisciplinary studies between cognitive researches and techniques that
encourage chance discovering.Also introducing an original study attempting to
revealthepreparedprocessofinsightproblemsolvingusingeyemovementdata,
they point out possible approaches from cognitive science to chance discovery.
All in all, the first part focuses on how we can open human’s mind out
to the value of external information. We have been endeavoring to reinforce
such cognitive forces toward chance discovery, by developing technical aspects
of human-machine and human-human interactions with developing techniques
of data visualization — as in the case KeyGraph has been used for discovery of
an opportunistic item in the market as shown in Fig. 1. In other words, data
visualizationplaystheroleastheenvironmentforchancecuration.Thatis,how
to present data on facts as evidences for viewers to notice chances — this is the
concept Chapter 1 introduced in this book and the second part embodies by
presentingfivecontributions.And,thisisthereasonwhywegivethetitle“Data
Visualization as Chance Curation” to the second part.
The second part starts from Chapter 4, where Sarlin writes an approach to
chancediscoveryinfinance,withself-organizingmapsfordiscoveringimbalances
in financialnetworks.Here we find the diffusionof financialcrisisfrom/tocoun-
tries and financial state transitions are easy to see on the visualized maps, so
that early signs of changes shall be caught as chances of people in businesses
VIII Preface
including investments andthe government.InChapter 5,Sunayamaet alshows
a recursive method for clustering and visualizing textual data, so that human
can detect useful information from the atlas of documents. As chance discovery
favors utility sometimes more than accuracy, their method which does not al-
ways present accurate clusters but provide useful information is expected to be
a promising tool for chance discovery.
Another feature of the second part is that communication is introduced as
a building block of chance discovery. In Chapter 6, Nitta presents a method
for analyzing discussions,coupling logicalanalysis of the discussion subject and
wordstatisticalanalysis—atemporalwordclusteringmethod—ofthelogtext
of discussion. We can appreciate this work as a way to integrate chance discov-
ery and logical modeling of the dynamics in the target domain. By integrating
thesetwomethods,thediscussionmoderationskillsareanalyzedtofindreusable
knowledge for other discussion. Furthermore,nonverbalinformation can be tar-
geted by extending Nitta’s method. In Chapter 7, Lin et al. visualizes graphs
representing the social network linked to innovation and its diffusion. By thus
visualized evidences, we can understand the dynamics of innovation and detect
essential events which, and early adopters who, may have caused or may cause
thegrowthofindustry.ThisapproachissucceedingtheessentialfeatureofKey-
Graph (Fig. 1), but their visualization will be found easier to see in interpret.
WangandOhsawa,inChapter8,presentsiChanceaWeb-basedenvironmentfor
innovative communication with chance curation. Here, the gaming principle of
InnovatorsMarketplace(cid:2) (thisisOhsawa’strademarkinUS:InnovatorsMarket
Game(cid:2) is a trademark in Japan) has been implemented on the Web and re-
inforced by Wang’s style of communication where inventors evaluate consumers
(aswellasviceversa)andhisnewvisualizationtechniqueGallaxy.Weareshow-
ing how chance discovery can be really realized by the collective intelligence of
humans and machines.
Let us look at the first and the second parts above from another aspect:
While humans perceive and understand chances and plan scenarios of actions
and events in the future, how are data processed? As in Fig. 2, we have been
modelingtheprocessofchancediscoveryasadoublespiral—humans’deepening
ofinterestsinchancesanddataprocessing(collectionandvisualizationaccording
to humans) interest.
We can say the first part of this book has been dedicated to the humans’
spiral, where collaborators communicate with curating chances, reframing, and
reaching insights to revise interests to adopt to the changing real world. And,
the second part extends our view to include technical aspects where machine
spiralare involved.Here we notice — the viewpoints of human(s) are revised in
steppingupthespiralsothatthedatainhandandcollectedfromexternalworld
areviewedandinterpretedina new direction.Thatis,in the case ofFig.1,two
clusters of textile items, which had been respectively regarded as materials for
casualclothesandfor businesssuits,came to be interpretedasone meta-cluster
created by combining the two existing clusters, by introducing a new viewpoint
tosatisfy businesspeople totakeeasyclothesafter theirworkingtime. Inshort,
Preface IX
Fig. 2. Double Helix (DH) process of chance discovery (also see
[Ohsawa and McBurney,2003] — not only chapter 1 but others are directly or
indirectly based on this process).
the thing which occurred here and is expected by following the DH process is
the synthesis of evidences, via the analysis and visualization of data.
Thethirdpartofthisbookmainlyhighlightstechniquesfordatamining,that
may seem to essentially focus on the analytical aspect of data processing. How-
ever, we position these work as computational and logical cutting edges for not
only dataanalysisbut alsodata synthesis.InChapter 9,Furuhata,Mizuta,and
So shows a method for tracking concept drift, for forecasting different types of
suddenunexpectedchangesandto beadaptivetothesechanges.Theirforecast-
ing method based on the stable evaluator and the reactive evaluator are good
at dealing with consecutive concept drifts. An application of this method is Fi-
nance, as tested in this paper to financial data in US including the late-2000s
recessions.InChapter10,Zhang,Leung,Pang,andTangproposesamulti-agent
system for finding satisfactory services.As this work is dedicated to the general
problem to match increasing providers and recipients efficiently, reader in any
service domain is expected to project this work to his/her own field e.g., how
can all patients find the best doctor for the health problem of each? In Chapter
11, Pogorelc and Gams focuses join health problem: Their technique enables to
discovercluesformedicaldecisionmaking—donothingorcallforhelp—from
data on daily movements of elderly people. Although their approach once pulls
us back to the traditional view point of data mining, that is classification accu-
racy, we notice there are domains where accuracy plays a critical role in quick
and suitable decision making. It is thus important to consider the use scenario
X Preface
where data are finally synthesized into decision making, in order to choose the
viewpoint in data analysis.
Thoughts about computation techniques sometimes give birth to ideas about
frameworks not only for artificial but also for natural (human’s) intelligence.
In Chapter 12, Vladimir Rybakov proposes a framework of temporal logics for
dealing with chance discovery. In chance discovery, the causality of events be-
fore and after a chance is uncertain, and only partially observable — the causal
logics behind them cannot be completely possible to explain and are hierarchi-
cally structured from local sequences and global scenarios. Rybakov proposes
a decidable logic of which the satisfiability is solvable, where operations of the
linear temporal logic are combined with introducing operators corresponding
to uncertainty temporally local and global discoveries. This will be not only a
basic of computation but a starting point to discuss in what logical framework
we should discuss chance discovery.In Chapter 13, Ogaardand March focus on
pilotbehaviorstakenfromaircrafttelemetry datacollectedbyamobileground-
basedsense-and-avoidsystemforUnmannedAircraftSystem(UAS)withsensors
and customized visualization software. In order to estimate the current risk of
midair collision, they developed probabilistic models for the behavior of pilots
of manned aircraft. Complex subpaths were discovered from the data using an
antcolony algorithm,andprobabilistic models were mined from those subpaths
by extending existing algorithms. Signs of failures may be detected objectively
by the method, and we may also expect a metacognitive effect i.e., pilots may
find mental subpaths that are latent in their own cognitive process which may
lead to unexpected risky behaviors. In Chapter 14, Hidenao Abe and Shusaku
Tsumoto presents a method for characterizing and clustering a large number of
documents, considering the features of temporal changes in the terms appeared
inadocumentcollection.Theyexperimentallyobtainedessentialfeaturesinthe
temporalbehaviorsoffourconferenceseriesondatamining,fromcorresponding
published document collections.
An extracted change may turn out to be a chance, if applied to documents
of one’s interesting domain. Finally in Chapter 15, Yoshiaki Okubo, Makoto
Haraguchi, and Takeshi Nakajima presents a method for finding an indicative
pattern, that is a rare but noteworthy pattern — an itemset consisting of sev-
eral general items but has a small degree of correlation. Then they discuss the
relationshipofindicativepatternsandchancepatterns,thatistheiroriginalab-
stractionofKeyGraph?whichhadbeencreatedbyOhsawa.Achancepatternis
anindicativepatternsupportedbyapairofmorefrequentbasepatternsBLand
BR. Such a pattern is expected to imply some hidden relationship between BL
andBR,asinthe wayKeyGraph(cid:2) visualizedbridgesbetweenfrequentpatterns
as in Fig. 1. They conclude that it might be required to take a causal relation-
ship betweena chance pattern anda base pattern into accountso thata chance
pattern can actually work as a valuable trigger for chance discovery.
Insummary,wecanpointoutthreemajorprogressesinchancediscoverysince
ourlastbookonchancediscovery[Ohsawa and Tsumoto, 2006]:First,basedon
our common sense that chance discovery is the multidisciplinary research field
Preface XI
based on at least two established domains (cognitive science and data mining
with visualization), we are involving fresh colleagues from application domains
and basic technologies and sciences. Application domains include health care,
aircraftcontrol,energyplant,managementoftechnologiesandfinances,product
designs/innovations,marketing,etc, andbasic technologiesandsciences include
sensor technologies, medical sciences, etc. Second, both cognitive scientists and
computational scientists who interacted with us deepened and extended their
fruits — they are too deep and wide to follow up in this narrow space. Third,
time came to be considered and introduced explicitly as a significant variable
ruling the causality from backgroundsituations to chances and from chances to
its impacts onevents and actions of humans in the future. Readers may urge us
tolistthe fourth,fifth,sixth,... progressesandwemaybe able todoso,but let
us stop here.
Aseditorsweexpectthereaderswillinvolvecolleagues,livingintherealworld
of human’s decisionmaking,to have them join our future activities — although
wedonotaimtobealargecommunitybutratherseektostaycompactenoughto
continuemeaningful synthesisofthoughts viacurationsof chancesandintimate
communications.
May 2012 Yukio Ohsawa
Tokyo Akinori Abe
References
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[Ohsawa and Usui, 2005] Ohsawa,Y.,Usui,M.:WorkshopwithTouchableKeyGraph
Activating Textile Market. In: Abe, A., Ohsawa, Y. (eds.) Readings in Chance
Discovery, Advanced Knowledge International (2005); note: The experiments for
this paper were conducted by2001
[Ohsawa and Tsumoto, 2006] Ohsawa, Y., Tsumoto, S. (eds.): Chance Discoveries in
Real World Decision Making, Data-based Interaction of Human Intelligence and
Artificial Intelligence. SCI, vol. 30. Springer(2006)
