Akinori Abe
From logical viewpoint
Recently, researches on discovery science and knowledge discovery
have been studied for various fields.
Indeed, these studies deal with real and large data, however
basically, they are types of learning that learn tendencies from the sets
of data in the same or similar categories.
Accordingly, they cannot foretell the events that are different from the
trend.
When we predict unknown events, it is quite unnatural to have a
stochastic model assumption that is made only from the known data and
ignores abnormal phenomena.
Of course, it is important to make a stochastic model to predict a
future feature, but it is more important to find factors that cannot
be reasoned by a stochastic process and are quite critical to an unknown
event.
That is, finding exception rules and exception facts is also
important to risk management or risk avoidance in a real world....
Chance Discovery is a research to discover chance!!! (For details pls
refer to to the CD site.)
By Ohsawa, chance is defined as follows;
A chance (risk) is a new event/situation that can be conceived either as an
opportunity or a risk.
I will approach Chance Discovery from the abductive viewpoint.
Definition: Chance
- Chance is a set of unknown hypotheses. Therefore,
explanation of an observation is not influenced by it. Accordingly, a possible
observation that should be explained cannot be explained.
In this case, a hypotheses base or a knowledge base lacks necessary hypotheses. Therefore,
it is necessary to generate missing hypotheses.
Missing hypotheses are characterized as chance.
- Chance itself is a set of known facts, but it is
unknown how to use them to explain an observation.
That is, a certain set of rules is missing.
Accordingly, an observation cannot be explained by facts.
Since rules are usually generated by inductive ways, rules that are different
from the trend cannot be generated.
In this case, rules are generated by abductive methods, so trends are
not considered.
Abductively generated rules are characterized as chance.
Abductive Analogical Reasoning (AAR) can generate missing hypotheses.
In a context of Chance Discovery, hypotheses generated by AAR
are regarded as chance in a sense defined in the above definition.
Therefore, it is nice to use AAR as a tool for chance discovery to
give some suggestion for possible sings of rare ot novel observation.
My standpoint is to apply Abductive Analogical Reasoning to Chance Discovery.
In the above definition, we defined two types of chance.
The followings show two types of chance discovery by using a framework of AAR.
- Type 1: When some of hypotheses are unknown.
When some of necessary hypotheses are unknown (not found), the current
observation cannot be explained.
In a context of chance discovery, chance seems to be a set of
unknown hypotheses. And this type of inference can be regarded as
pure abduction.
- Type 2: When some of rules are unknown.
When some of necessary rules are unknown, even if we are aware of all of
symptoms, the future observation cannot be explained (predicted).
In this case, usually, reasons are shown afterwards.
This type of inference can be regarded as AAR.
The first one is to show unseen or unknown events as chance and the second
one is to show the known events as chance by generating new rules.
Both predictions can be done by abduction from the possible
observations. Therefore, from this
formalization, a chance seems to be a set of abductive
hypotheses that is generated in a logical way.
Actually, in this framework, since abduction is an inference
from an observation to hypotheses, it seems that all possible
observations are necessary to
be prepared. However all possible observation do not need to be prepared.
We only need to explain or predict our concerning observations.
From psychological viewpoint
- Chance Discovery in Multi-lingual Translation Site
Recently, the data on web site has been thought of as meaningful
data to analyze our society. It is used to see the trends in specific
field. In addition, it can be used to predict the next trend or
current hidden needs. This is because the data is natural (unintentional).
We run the multi-lingual machine translation service site.
In fact, in more than one year, we have
collected huge mount of log data. They are user's access data, user's
translation data (word, text, url, language pair etc.), and user's
feedback comments. From these data, we can obtain a lot of information.
We analyzed log data to find a certain chance.
From the viewpoint of commercially maintaining machine translation
site, chance will be new business model. On the other hands, from the
viewpoint of sociology, chance will be results from the change of
user's interests. They reflect the situation of society and if we use
them correctly, we can guess novel or rare events before they
explicitly appear in the world.
(business) chance:
-
The users do not frequently search their interesting page by
using search
engine, but they check magazine or come from linked pages.
Therefore, very famous site like {\tt www.asahi.com} and {\tt
www.cnn.com} can live long. However, other infamous site should
register not on search engine but on special site and special media
like magazine or TV.
-
Language pair should be English-centered. Actually, translation
services between Chinese and Japanese and between Korean and Japanese
are required, if we have translation services between Chinese and
English and between Korean and English, it will be better for the
users in the world. This is because, recently, Asian countries are
thought of as a significant place from the various viewpoint like
economy, culture etc.
- User's interests change
Recently, the necessity to the assistance of creative work has been
increased. Creative works are usually regarded as specialized
skills. However, from the viewpoint of chance discovery, creative work
can be thought of as chance.
According to the definition of (chance), the creative work can be thought of as chance.
This is because creativity means novel (not rare) and creative work
comes from nothing or from something relative but whose relation was
hidden.
This research showed chance as creative work, and shows the process of
chance discovery. This formalization comes from our
experience. We work in abroad and
does not bring enough references and there is not proper libraries
around office and house. When he wrote paper, he naturally used an
internet search engine like yahoo. Each search engine
has its own feature. Therefore we must know their feature to obtain
proper results. This task will be a sort of craftsman performance.
Sometimes search engine returns different result from our
intention. Usually, such wrong results are
useless. However, in some cases, the wrong result leads us novel
thinking or confirm our original target.
We modeled this type of process as a chance discovery process.
Applications of Chance Discovery
- Creativity support
See above.
In addition, we are taking into consideration an aspect of chance
discovery to the Augmented Music Composition Support that supports
music composition by showing differnence between the composition of
experts and that of the users.
- Chance Discovery in Nursing Informatics (NI)
Logical view:
Abst: We analyze the feature of chance in nursing risk
management for better management.
Recently, it has been recognized that medical risk management is
very important both for hospitals and hospital patients.
To reduce nursing accidents, examples of nursing accidents are usually
collected for analysis. This allows us to obtain certain tendencies
of nursing accidents and causality between environment and
nursing accidents.
Such knowledge can be useful in nursing risk management, but is not
fully adequate.
In addition, in a real situation, it is necessary to deal with a
hidden relationship between an ignored event or factor and an accident.
Thus, we analyze the feature of hidden factors in
nursing accidents and propose a way to determine chance (= hidden or
ignored factors) as abductive hypotheses.
***Nursing risk prediction as Chance Discovery***
Risk management itself can be thought of as an application of
Chance Discovery. A chance in nursing risk management can be
defined as an ``ignored'' event, factor, environment, personal
relationship or
personal matter that has the possibility to cause a serious nursing
accident or incident in the future.
in the case of a slip,
due to unexpected events, although an ideal or desired situation has not
actually been achieved, we unconsciously convince ourselves that we have
achieved such an ideal or desired situation.
Thus because of (unintentional) discontinuation of
tasks, we cannot keep our contexts.
A chance exists at the hatched circle in the above figure.
How to determine a chance?
It is difficult to detect a critical point beforehand. However, it would be
possible to abductively detect a critical point as a point where an
inconsistency between an ideal result and his/her actual activity occurs.
For example, if we use a hypothetical reasoning framework such as Theorist,
we might be able to deal with the situation.
In the case where we know all of the possible hypotheses and their ideal
observations, we can detect malpractice. This is because if he/she
selects a wrong hypothesis set, an ideal observation cannot be
explained. A simple framework is shown below:
Linguistic view:
- Chance Discovery in Medical Informatics (MI)
Regarding medical diagnosis support system,
the problem of knowledge base incompleteness makes it necessary to combine
an inference part based on abduction and a knowledge acquisition and
learning part based on induction.
We think it is necessary to do an automatic medical rule
generation method from the viewpoint of induction and chance
discovery, to do abduction for chance discovery, and to seek for
possible methods including KeyGraph to achieve medical chance discovery.
- Chance Discovery in Medical Informatics (MI)
Regarding medical diagnosis support system,
the problem of knowledge base incompleteness makes it necessary to combine
an inference part based on abduction and a knowledge acquisition and
learning part based on induction.
We think it is necessary to do an automatic medical rule
generation method from the viewpoint of induction and chance
discovery, to do abduction for chance discovery, and to seek for
possible methods including KeyGraph to achieve medical chance discovery.
Our definitions of chance in MI:
- A phenomenon that is slightly different from the typical phenomenon.
This can be regarded as a symptom in the gray zone. The
knowledge is similar or close to the typical one. It might be
regarded as an exception if we extend the definition of exception.
- A supporting phenomenon that is quite rare or novel,
so we cannot think an existing or known knowledge works for a reason.
In this case, the result (observation) is explicit, but we
cannot find any explanations or reasons (hypotheses in abduction) for the
result.
- A phenomenon that is so rare or novel that we have no
knowledge to deal with it, therefore, we cannot infer any results.
In this case, in spite of any symptoms, results are implicit.
This is because we do not have any knowledge from which to diagnose
from the symptoms.
To do chance discovery in MI, we can use logical methods etc.
- the role of induction: to detect the gray zone as well as to
build a standardized knowledge base.
- the role of abduction: to show (suggest) known events as chance by
generating new or novel knowledge.
- the role of KeyGraph: graphically outputs a relationship among
elements in a data set: to find rare or hidden relationships.
Currently, we have the following conclusions;
Since disease is an outbreak with many factors, such as virus and
environment, etc., it is quite difficult to find a chance
from only medical data. Consequently, we need to build a medical
knowledge base with consideration of the environment. Similarly, we need to
make an inference with consideration of the environment. We think a
relationship between medical data and the environment is hidden,
so it is necessary to find such a hidden relationship by
abduction and KeyGraph.
Curation (new direction)
Curation
In general, curators have responsibilities for various aspects of exhibition
activities. However, the most important activity will be a plan of exhibition.
For that the above activities such as research, interpretation and
acquisition are necessary. They should properly exhibit a truth which
is result of their researches and interpretations.
From the exhibition ``Bacon and Caravaggio'' in Museo e Galleria Borghese, Roma, Italy (2009):
Coliva pointed out
``this exhibition proposes a juxtaposition of Bacon and Caravaggio. It
intends to offer visitors an opportunity for an aesthetic experience
rather than an educational one. [...] An exhibition of generally conceived and prepared with a historicist
mentality, but when it materializes, the simultaneous presence of the
works --- in the sense precisely of their hanging --- opens up
parallels and poses very complex and spontaneous questions, which may
even be unexpected and not all stem exactly from questions initially
posed by art-historical motives and theses. There are parallels that
appear by themselves to the visitor's sensibility and are not imposed
by a theory of the curator. This is certainly one aspect of the
vitality of exhibitions, which make the works live and in this are
necessary for the works. The display itself, in the sense of the
presentation of the works that appear in an exhibition ---the spectacle
of their being on display --- creates trains of thought that are
independent of the interpretations provided by art-historical
scholarship. And since for a profound experience of understanding a
work these ramifications sometimes are more surprising and significant
than the achievements of a specialized scholarship in its own field of
action, an art raised to the status of an enigma like Bacon's seems to
require the gamble of provoking these parallels. And since at the
time, and again because of its qualitative greatness, Caravaggio's art
deserves a similar provocation, the juxtaposition thus satisfies a
legitimate aesthetic desire. On the other hand, the juxtaposition is a
modest and prudent solution, not so much for demonstrating, but for
offering the attribute of ``genius'' --- which the expressive common
language attributes to the great artist of the past --- opportunities
to manifest itself. And the juxtaposition is induced by the Galleria
Borghese itself, one of the most sensitive spaces with the simultaneous
presence of genius.''
Besides the importance in aesthetics and philosophy, I think the most
important point is that ``There are parallels that
appear by themselves to the visitor's sensibility and are not imposed
by a theory of the curator.'' That is, though actually a curator has a
certain philosophy, he/she does not insist his/her philosophy but
audiences will be able to discover additional meanings as well as
the curator's intended philosophy.
Curation and chance discovery:
``Chance discovery'' research is a type of research to establish
methods, strategies, theories, and even activities to discover a
chance. In addition, it aims at discovering human factors for chance
discoveries.
Therefore not only researchers in computer science and engineering but
also researchers with different expertise such as psychologists,
philosophers,
economists and sociologists take part in chance discovery research.
Thus it is very important to offer opportunities where receivers can
feel and obtain chances in various situations.
Many applications on chance discover have been proposed in these 10
years.
However, strategies how to display chances have not been discussed in
many applications.
Strategy for discovering chances is of course important.
In addition, strategy for an easy discovery interface of chances is
more important. The above interface based application can be
classified to curation type applications.
My experience in a market store ---meats (chicken) and asparagus were sold
together--- can be regarded as a type of chance
discovery application, because the strategy generated a hidden or potential
purchase chance to customers. Customers who were inspired by the
combination of chicken and asparagus would have bought either or both of
them for dinner. Actually, this is not a task in museums, but it can be also
regarded as a curator's work (curation). Because the strategy includes
philosophy in a combination of items, and based on the philosophy it
will offer certain effects to audiences.
Visualization strategies such that referred to above function as
curation. Because they display candidate chances in a manner where
important or necessary items or events can be easily or interactively
discovered by the user.
Curation of the business situation in the internet age:
Sasaki defined curation as follows:
Curation: From huge amount of information source, according to
the curator's sense of value and world view, picking up information,
giving a new meaning to it and sharing it with many persons.
Sasaki used a metaphor of biotope to illustrate the promotion of a
unknown or less known but a good artist.
A ``curation'' for business in the network age seems an interaction
between customer (user) and goods. There will not be a system to
insist trends from big companies, but trends will be constructed
or selected according to customers' interaction on networks. In
addition, a (small) company or community can use this system to give
rare goods a certain trend.
That is, no curation is directly performed by a ``system manager.''
Instead a ``system manager'' tries to use a certain community effectively.
A ``system manager'' provides a certain information to those who will
be interested in it. The information will be shared in the community
and sometimes it will be delivered to other communities having the same
or similar interests. There exist a
certain intention, but the feature of curation is rather vague and
changeable. Perhaps the task as a curation is to offer a certain
environment for communication among customers.
Thus generation of customers communication is important in this type
of curation.
[New definition of curation in chance discovery]
- Curation is a task to offer users opportunities to discover chances.
- Curation should be conducted with considering to offer implicit and
potential possibilities.
- Chances should not be explicitly displayed to users.
- However, such chances should rather easily be discovered and
arranged according to the user's interests and situations. This can be
achieved for instance by affordance.
- There can be a certain holistic communication environment. This
type of holistic communication might function as media to
discover chance for novice users.
- There should be a certain freedom for user to interpret
a key person, matter, thing or event, which should only stimulate or
assist users' thinking procedure.
- There should be a certain freedom for user to arrange chances.
References:
- Abe A.: The role of abduction in Chance Discovery,
Proc. of SCI2001, Vol. VIII, pp. 400-405 (2001)
- Abe A.: User's interests change as Chance Discovery,
Proc. of KES2002, pp. 1291-1295 (2002)
- Abe A., Toong C. K., Nakamura M., Tsukada M., and Kotera H.:
Finding Chance in Multi-lingual Translation Site,
Proc. of AAAI Fall Symp. on Chance Discovery: The Discovery and
Management of Chance Evnet (FS-02-01), pp. 22-27 (2002)
- Abe A.: Is forecasting harder than it used to be?, panel discussion in
AAAI Fall Symp. on Chance Discovery (2002) slide
- Abe A.: The role of abduction in Chance Discovery,
New Generation Computing, Vol.21, No.1, pp. 61-71 (2003)
- Abe A., Kogure K. and Hagita N.: Discovery of Hidden Relations from Medical Data,
Proc. of HCI2003 3rd. Int'l Workshop on Chance Discovery, pp. 37-43 (2003)
- Abe A.: Abduction and Analogy in Chance Discovery, in
Chance Discovery (Osawa Y. and McBurney P. eds.), Chap. 16, pp. 231-248, Springer (2003)
- Ē ūT: zI_Æ`XĐ, in
`XĐĖîņZp (åāV Kķ Ō), æ 8 Í, d@åwoÅĮ (2003)
- Abe A., Berry R., M. Suzuki, and Hagita N.:
Augmented Music Composition Support as Active Mining,
Technical Report of IEICE, Vol. 103, No. 304, pp.59-64
(Technical Report of JSAI, SIG-KBS-A301, pp.59--64) (2003)
paper
- Abe A. and Oehlmann: From Data Mining to Interpersonal
Communication for Scenario Development,
Proc. of ECAI2004 Workshop on Chance Discovery, pp. 1--8 (2004)
- Abe A., Kogure K. and Hagita N.: Determination of A
Chance in Nursing Risk Management, Proc. of ECAI2004 Workshop on
Chance Discovery, pp. 222-231 (2004)
- Abe A., Kogure K. and Hagita N.: Nursing Risk
Prediction as Chance Discovery, Proc. of KES2004 Vol. II,
pp. 815--822 (2004).
- Abe A. and Ohsawa Y. eds:
Readings in Chance Discovery,
International Series on Advanced Intelligence (2005)
- Abe A., Toong C. K., Nakamura M., C. W. Lim, Tsukada M., and
Kotera H.: Finding Chance in Multi-lingual Translation Site,
in Readings in Chance Discovery (Abe A. and Ohsaw Y. eds.), Chap. 3,
pp. 25--36 (2005)
- Abe A.: Creativity as Chance Discovery, Proc. of
JCIS05 1st Annual Workshop on Rough Sets and Chance Discovery,
pp. 1782--1785 (2005)
- Abe A., Naya F., Ozaku H.I., Kuwahara N., and Kogure
K.: Scenario Violation in Nursing Activities, Proc. of the ICML05 4th Int'l
Workshop on Chance Discovery, pp. 102--109 (2005)
- Abe A., Naya F., Ozaku H.I., Sagara K., Kuwahara N.,
and Kogure K.: Risk Management by Focusing on Critical Words in
Nurses' Conversations, Proc. of KES2005, Vol I, pp. 1167--1173 (2005)
- Abe A., Ozaku H.I., Kuwahara N., and Kogure K.:
Scenario-base Construction for Abductive Nursing Risk Management,
Proc. of IPUM2006, pp. 206--213 (2006)
- Abe A., Ozaku H.I., Kuwahara N., and Kogure K.:
What Should be Abducible in Abductive Nursing Risk Management?,
Proc. of KES2006 (LNAI4253), Vol III, pp. 22--29 (2006)
- Abe A., Ozaku H.I., Kuwahara N., and Kogure K.:
Relation between Abductive and Inductive Nursing Risk Managements,
Proc. of RM2006 (JSAI2006), pp. 121--132 (2006)
- Abe A. and Kogure K.: E-Nightingale: Crisis detection
in nursing activities, in
Chance Discoveries in Real World Decision Making (Ohsawa Y. and
Tsumoto S. Eds.), Data-based Interaction of Human intelligence and
Artificial Intelligence Series: Studies in Computational
Intelligence, Chap. 15, pp. 357--371, Vol. 30, 2007, XIV, 404 (2006)
- Abe A., Ozaku H.I., Kuwahara N., and Kogure K.:
Cooperation between Abductive and Inductive Nursing Risk Management,
Proc. of RM2006 (ICDM2006), pp. 705--708 (2006)
- Abe A., Ozaku H.I., Kuwahara N., and Kogure K.:
Scenario Violation in Nursing Activities
--- Nursing Risk Management from the viewpoint of Chance Discovery,
Soft Computing Journal, Springer, Vol. 11, No. 8, pp. 799--809 (2007)
- Abe A., Ozaku H.I., Kuwahara N., and Kogure K.:
Relation between Abductive and Inductive Types of Nursing Risk Management,
Post-proc. of JSAI2006 (LNAI 4834), pp. 387--400 (2007)
- Abe A., Ozaku I.H., Ohsawa Y., Sagara K., Kuwahara N.,
and Kogure K.: Communication error determination model for multiply
layered situations, Proc. of RI2007 (JSAI2007), pp. 38--48 (2007)
- Abe A., Ozaku H.I., Sagara K., Kuwahara N., and Kogure K.:
Nursing Risk Management by Focusing on Critical Words or
Phrases in Nurses' Conversations, Int'l J. of Knowledge-based and
Intelligent Engineering Systems, Vol. 11, No. 5, pp. 281--289 (2007)
- Abe A., Ohsawa Y., Ozaku H.I., Sagara K.,
Kuwahara N., and Kogure K.: Communication error determination model
for multi-layered or chained situations, Proc. of PAKDD 2008
Working Notes of Workshops on Data Mining for Decision Making and
Risk Management, pp. 305--316 (2008)
- Abe A, Hagita N, Furutani M, Furutani Y, and Matsuoka R.:
Categorized and Integrated Data Mining of Clinical Data,
in Communications and Discoveries from Multidisciplinary Data
(Iwata S., Ohsawa Y., Tsumoto S., Zhong N., Shi Y., Magnani L. eds.),
Studies in Computational Intelligence, Vol. 123, pp. 315--330,
Springer Verlag (2008)
- Abe A., Hagita N., Furutani M.,
Furutani Y., and Matsuoka R.: Exceptions as Chance for Computational
Chance Discovery,
Proc. of KES2008 (LNAI5179), Vol. II, pp. 750--757, Springer
Verlag (2008)
- Abe A.: Cognitive Chance Discovery, in Universal
Access in Human-Computer Interaction --- Addressing Diversity
(Stephanidis eds.), UAHCI 2009, Held as Part of HCI International
2009, Proceedings, Part I (LNCS5614),
pp. 315--323, Springer-Verlag (2009)
- Abe A., Hagita N., Furutani M., Furutani Y., and
Matsuoka R.: An Interface for Medical Diagnosis Support ---from the
viewpoint of Chance Discovery, International Journal of
Advanced Intelligence Paradigms, Vol. 2, No. 2/3, pp. 283--302 (2010)
- Ohsawa Y., Abe A., and Nakamura J.: Chance
Discovery as Analogy based Value Sensing, International Journal
of Organizational and Collective Intelligence, Vol. 1, No. 1, pp. 44--57 (2010)
- Abe A., Ohsawa Y., Ozaku I.H., Sagara K., Kuwahara N.,
Kogure K.: Communication Error Determination System for Multi-layered or
Chained Situations, Fundamenta Informaticae, 98, pp. 123--142 (2010)
- Abe A., Ohsawa Y., Kuwahara N., Ozaku I.H., Sagara K.,
Kogure K.: Scenario Violation as Gaps between Activity Patterns,
New Mathematics and Natural Computation, Vol. 6, No. 2, pp. 193--208 (2010)
- Abe A., Hagita N., Furutani M., Furutani Y., and
Matsuoka R.: Categorized and Integrated Data Mining of Medical Data
from the Viewpoint of Chance Discovery, Proc. of KES2010
(LNAI6278), Part III, pp. 307--314, Springer Verlag (2010) to appear
- Abe A.: Abduction dealing with potential values, Proc. SMC2010, pp. 1279--1285 (2010)
- Abe A.: Curation in Chance Discovery,
Proc. ICDM2010 5th International Workshop on Chance Discovery, pp. 793--799 (2010)
- Abe A., Hagita N., Furutani M., Furutani Y., and
Matsuoka R.: An interactive interface for medical diagnosis support,
in Sequence and Genome Analysis: Methods and Applications
(Zhongming Zhao eds.), Chap. 17, pp. 289--305, iConcept Press (2011)
- Abe A.: Curation and Communication in Chance
Discovery, Proc. of IJCAI2011 6th International Workshop on
Chance Discovery, pp. 3--8 (2011)
- Abe A.: Relation between chance discovery and Black
Swan awareness,
Proc. of KES2011 (LNAI6882), Part II, pp.495--504, Springer Verlag (2011)
- Abe A.: Curation in Chance Discovery, in
Abe A. and Ohsawa Y. eds.: Advances in Chance Discovery, SCI 423,
pp. 1--18, Springer Verlag (2012)
- Abe A.: Cognitive Chance Discovery: from abduction to
affordance, in Philosophy and Cognitive Science (Magnani L. and
Li L. eds), SAPERE 2, pp. 155--172, Springer Verlag (2012)
- Abe A.: Chance discovery and black swan: from the viewpoint of
abduction and affordance,
Proc. of KES2012, pp. 1638--1645, IOS Press (2012)
- Abe A.: Curation in chance discovery again, Proc. of
EWCDDS12, pp. 37--42 (2012)
- Abe A. and Ohsawa Y. eds.: Proc. 1st European
Workshop on Chance Discovery and Data Synthesis (EWCDDS12) (2012)
- Abe A.: Visualization as Curation with a holistic
communication, Proc. of TAAI2012, pp. 266--271 (2012)
- Abe A.: Data Mining in the Age of Curation,
Proc. of IEEE International Workshop on Data Mining for Service (DMS2012), pp. 273--279 (2012)
- Abe A.: Relation between chance discovery and Black Swan awareness: from the viewpoint of abduction and affordance, International Journal of Knowledge and Systems Science (IJKSS), Vol. 4, No. 1, pp. 62--76 (2013)
- Abe A.: Shikake as affordance and curation in chance
discovery, Proc. of AAAI2013 Spring Symposium on Shikakelology, SS-13-06, pp. 2--10 (2013)
- Abe A.: Cognitive Chance Discovery: from Abduction to Affordance and Curation, Proc. of ICCI*CC2013. pp. 308--314 (2013)
- Abe A.: Relationship between curation, chance and shikake,
Proc. of KES2013, pp. 1219--1228, Elsevier (2013)
- Abe A.: Curating and mining (big) data, Proc. of
ICDM2013 Workshop on MoDAT, pp. 664--671 (2013)
- Kubo M. and Abe A.: Critical Points in Conversations from the Perspective of Chance Discovery, Proc. of KES2014, pp. 969--978 (2014)
- Abe A.: Data mining considering curation, Proc. of WIC2014, pp. 386--391 (2014)
- Ļ OM: f[^ŠÍÉĻŊéņūĶIUąĖĪ, lHm\wïĪïŋ, SIG-LSE-B402-3, pp. 21--24 (2014)