Chance Discoveries in Real World Decision Making: Data-based Interaction of Human Intelligence and Artificial Intelligence

401 Pages·2006·17.86 MB·English

by  Prof. Dr. Yukio Ohsawa (auth.), Prof. Dr. Yukio Ohsawa, Prof. Dr. Shusaku Tsumoto (eds.)

Yukio Ohsawa, Shusaku Tsumoto (Eds.)
Chance Discoveries in Real World Decision Making
Studies in Computational Intelligence, Volume 30

Editor-in-chief
Prof. Janusz Kacprzyk
Systems Research Institute
Polish Academy of Sciences
ul. Newelska 6 (cid:1)
01-447 Warsaw
Poland
E-mail: [email protected]

Further volumes of this series Vol. 22. N. Nedjah, E. Alba, L. de Macedo
can be found on our homepage: Mourelle (Eds.)
springer.com Parallel Evolutionary Computations, 2006
ISBN 3-540-32837-8
Vol. 13. Nadia Nedjah, Ajith Abraham,
Vol. 23. M. Last, Z. Volkovich, A. Kandel (Eds.)
Luiza de Macedo Mourelle (Eds.)
Algorithmic Techniques for Data Mining, 2006
Genetic Systems Programming, 2006
ISBN 3-540-33880-2
ISBN 3-540-29849-5

Vol. 24. Alakananda Bhattacharya, Amit Konar,
Vol. 14. Spiros Sirmakessis (Ed.)
Ajit K. Mandal
Adaptive and Personalized Semantic Web, 2006
Parallel and Distributed Logic Programming,
ISBN 3-540-30605-6
2006

Vol. 15. Lei Zhi Chen, Sing Kiong Nguang, ISBN 3-540-33458-0
Xiao Dong Chen
Vol. 25. ZoltÆn (cid:201)sik, Carlos Mart(cid:237)n-Vide,
Modelling and Optimization of
Victor Mitrana (Eds.)
Biotechnological Processes, 2006
Recent Advances in Formal Languages
ISBN 3-540-30634-X
and Applications, 2006
Vol. 16. Yaochu Jin (Ed.) ISBN 3-540-33460-2

Multi-Objective Machine Learning, 2006
ISBN 3-540-30676-5 Vol. 26. Nadia Nedjah, Luiza de Macedo Mourelle
(Eds.)
Vol. 17. Te-Ming Huang, Vojislav Kecman, Swarm Intelligent Systems, 2006
Ivica Kopriva ISBN 3-540-33868-3
Kernel Based Algorithms for Mining Huge
Data Sets, 2006 V ol. 27. Vassilis G. Kaburlasos
ISBN 3-540-31681-7 Towards a Unified Modeling and Knowledge-
R epresentation based on Lattice Theory, 2006
Vol. 18. Chang Wook Ahn ISBN 3-540-34169-2
Advances in Evolutionary Algorithms, 2006
ISBN 3-540-31758-9 V ol. 28. Brahim Chaib-draa, J(cid:246)rg P. M(cid:252)ller (Eds.)
Multiagent based Supply Chain Management,2006
Vol. 19. Ajita Ichalkaranje, Nikhil
I SBN 3-540-33875-6
Ichalkaranje, Lakhmi C. Jain (Eds.)

Intelligent Paradigms for Assistive and V ol. 29. Sai Sumathi, S.N. Sivanandam
Preventive Healthcare, 2006 Introduction to Data Mining and its
ISBN 3-540-31762-7 A pplication, 2006
ISBN 3-540-34350-4
Vol. 20. Wojciech Penczek, Agata Półrola
Advances in Verification of Time Petri Nets V ol. 30. Yukio Ohsawa, Shusaku Tsumoto (Eds.)
and Timed Automata, 2006 Chance Discoveries in Real World Decision
ISBN 3-540-32869-6 Making,2006
I SBN 3-540-34352-0
Vol. 21. C(cid:226)ndida Ferreira
Gene Expression on Programming: Mathematical
M odeling by an Artificial Intelligence,2006
ISBN 3-540-32796-7
Yukio Ohsawa
Shusaku Tsumoto (Eds.)

Chance Discoveries in
Real World
Decision Making
Data-based Interaction

of Human Intelligence

and Artificial Intelligence

With 161 Figures and 46 Tables
123
Prof. Dr. Yukio Ohsawa
Graduate School of Engineering
The University of Tokyo
7-3-1 Hongo, Bunkyo-Ku
Tokyo 113-8656
Japan
E-mail: [email protected]
Prof. Dr. Shusaku Tsumoto
Department of Medical Informatics:
School of Medicine, Shimane University
89-1 Enya-cho, Izumo-city
Shimane 693-8501
Japan
E-mail: [email protected];
[email protected]

Library of Congress Control Number: 2006926224

ISSN print edition: 1860-949X
ISSN electronic edition: 1860-9503
ISBN-10 3-540-34352-0 Springer Berlin Heidelberg New York
ISBN-13 978-3-540-34352-3 Springer Berlin Heidelberg New York

This work is subject to copyright. All rights are reserved, whether the whole or part of the mate-
rial is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recita-
tion, broadcasting, reproduction on microfilm or in any other way, and storage in data banks.
Duplication of this publication or parts thereof is permitted only under the provisions of the
German Copyright Law of September 9, 1965, in its current version, and permission for use
must always be obtained from Springer-Verlag. Violations are liable to prosecution under the
German Copyright Law.

Springer is a part of Springer Science+Business Media
springer.com
© Springer-Verlag Berlin Heidelberg 2006

The use of general descriptive names, registered names, trademarks, etc. in this publication
does not imply, even in the absence of a specific statement, that such names are exempt from
the relevant protective laws and regulations and therefore free for general use.

Cover design: deblik, Berlin
Typesetting by the authors and SPi
Printed on acid-free paper SPIN: 11588757 89/SPI 5 4 3 2 1 0
For everyone seeking opportunities and fearing catastrophe
Preface
Of late, researchers in artificial intelligence have become interested in events
and situations that affect human decision making, in that they are viewed
as opportunities or risks. A chance is an event or a situation, which may be
significantforhumandecision-inotherwordsitprovidesanopportunityora
risk for human decision making and problem solving. Noticing such an event
is described as chance discovery, i.e., discovery of a chance. In this sense, the
discoveryofachanceisemphasizedincontrasttodiscoverybychance.Chance
discoveryismeaningfulfromtheviewpointofvariousreal-worlddomains.For
example,chancediscoveryinmedicinecanbeviewedasawaytofindasuitable
opportunity for taking an effective treatment or checking the possibility of a
fatal progress of disease.
Chance discovery focuses especially intense attention to rare or novel
events, as far as they may be important for a critical decision, and take care
not to regard them as sheer noise. In contrast to the power of the machine
to learn patterns from rich data, human sense to be aware of the significance
of a present event is miraculously strong. Because the essential aspect of a
chance is that it can be the seed of new and significant benefits/loss in the
near future, the human sense on benefit and loss is a significant factor for
chance discovery as well as the machine’s power to analyze data.
Sometimes a chance turns out to be a rare event. In medicine, the most
suitable occasion for a critical action tends to be rare. Until now, a new fea-
ture of chance discovery has been computational results leading the user to
a successful understanding of “low-probability” (rare) events, whereas many
former methods of data analysis and data mining were dedicated to learn-
ing “high-probability” (frequent) patterns among frequent events. Recently,
several approaches have been developed for predicting rare events. However,
the focus of chance discovery is to detect present low-probability (sometimes
zero-probability in the data) events that can cause near-future explosion of a
significant event. Going back to the example of chance discovery in medicine,
the patterns that a medical doctor would like to find may be temporal trends
that predict risky events, and also risky events that predict the appearance
VIII Preface
of fatal trends. The former is hard for a computer due to the lack in the
quantityofdata,andthelatterisevenharderbecauseacomplexcausalchain
with hidden parts may be the source of such a catastrophe. Thus, the doctor
requires his/her sensitivity and the ability to communicate with the patient,
for really discovering important events for the decision of treatment.
Since 2000 when we started this field, we have organized a number of
workshops on chance discovery and published books, where the process of
chance discovery has been characterized in terms of (1) becoming aware of a
chance (2) explaining its significance for humans, and (3) deciding and doing
real actions. These steps may interleave each other, not necessarily running
in the fixed order like “(1), (2), and then (3).” Computer-aided visualization
of events on real data from the environment may contribute to (1), human
expertise is absolutely relevant to (2), but the real data and human expertise
areobtainedfromhis/heractionsintherealenvironmentin(3)thatlookslike
to come after (1) and (2). Thus the interaction among human(s), computers,
and the environment is the core of studies for chance discovery.
We mentioned “human(s)” because it is also important to study the so-
cial interaction among humans. When considering medicine for example, it
becomes apparent that a chance event such as a noteworthy symptom can be
understood only by learning the essential nature of the applied domain un-
derlying the observed events. Thus, the knowledge and expertise of humans
are important because a hidden essence cannot be recorded in the data. For
externalizing the tacit dimension of human expertise, human communication
plays a significant role.
With these backgrounds, in the special issues in journals and workshops
we organized so far on chance discovery, contributors showed a variety of
approaches to chance discovery from three Cs: computation, communication,
and cognition.
Forthisbook,weinvitedandcalledforcontributionsfromimportantareas
relevanttotheprocessofchancediscovery.Inthecallforpapers,wementioned
thatthespecialinterestwouldbeonanalysisofhumanbehaviorandcomplex
systems, including the following
- theanalysisofinteractionsbetweenindividualsorgroupsandtheenviron-
ment,
- the development of intelligent support tools for chance discovery,
- the relation between chance discovery and creativity
- the development of the logical foundations of chance discovery, and
- the identification of the characteristics of a chance.
As a result, the contributions we received were on
- identifying rare or novel events, and becoming aware of significant events
- predicting future trends
- integrating knowledge of significant events with existing knowledge
- explaining hidden events
Preface IX
- communicating for scenario development
- team work support
- creativity support
- marketing and sales support
- side-effects of new drugs, etc.
Theauthorsofthesecontributionsspecializedinareasrangingfrominfor-
mation sciences such as artificial intelligence, creativity research, economics,
businessadministration,operationresearch,linguistics,mathematics,physics,
andpsychology.Thesesubmissionscutacrossthebordersofagivendiscipline
andinvolvedperspectivesfromdifferentfieldsoranintegrationoftheoriesand
applications.
Among the 31 papers submitted, we selected 10. By including 13 invited
studiesfromcontributorssofar,the23finalpapersappeared.Ihopethereader
finds an interesting feature common to these papers: They do not necessarily
evaluate the research result on the established criteria that some scientists
would state are “objective.” In the review process, we rather encouraged ac-
cept area of creative methods and theories for chance discovery, as far as the
utility of a discovered chance can be admired for producing the future in hu-
man life. The reader will find new visions in these studies for decision makers
to create new trends leading to rich human life based on the interaction of
human, machine, and man’s living environment, rather than predictions on
thefuturebasedexclusivelyonpastobservationalpatterns.Wecansaythisis
a promising new trend in computer-supported decision of human, where the
future might/should be changed radically from the past.
Readers will also find data analysis methods developed or under devel-
opment for aiding human process of chance discovery. Some of the meth-
ods in this book are evaluated objectively by small datasets, and others are
evaluated subjectively by users working in their real business. Readers are
recommended to note that methods not covered in this book, such as de-
tecting exceptional rules, clustering methods using rough sets, link discovery
methods, computation for obtaining meaningful outliers, etc., can be candi-
date tools for aiding chance discovery, as long as they are involved in the
process of human–machine–environment interaction for chance discovery. By
widening our toolbox, we can extend the application domains of chance dis-
covery to marketing, product design, finance, fingerprint detections, etc. We
will continue workshops and publications on chance discovery, and related
new research, expecting new researchers to bring new tools to the box, and
also users with boxes to fill in, i.e., new real-world domains seeking methods
of chance discovery.
In Tokyo and Shimane, Japan Yukio Ohsawa
April 2006 Shusaku Tsumoto
Contents
Part I Basis
Chance Discovery: The Current States of Art
Yukio Ohsawa ................................................... 3
The Structure of Scenario Communication and Chance
Discovery
Katsutoshi Yada ................................................. 21
Complexity in the Discovery of Chance: Daily Life Criticality
Calkin S. Montero, Kenji Araki.................................... 37
Part II Computational
Chance Discovery
H.M. Hubey ..................................................... 49
Scenario Maps on Situational Switch Model, Applied to
Blood-Test Data for Hepatitis C Patients
Yukio Ohsawa ................................................... 69
Uncertainty Reasoning and Chance Discovery
Kyung-Joong Kim, Sung-Bae Cho.................................. 83
Digital Modulation Classification using Fuzzy Neural
Networks
Wenbo Zong, Edmund M-K. Lai, Chai Quek.........................101
Chance Discovery using Complementary Learning Fuzzy
Neural Network
T.Z. Tan, C. Quek, G.S. Ng.......................................117
XII Contents
Part III Communication
Modeling Influence Diffusion in Human Society
Naohiro Matsumura ..............................................137
Resonance without Response: The Way of Topic Growth in
Communications
Yukio Ohsawa, Naohiro Matsumura, Koichi Takahashi................155
Scenario to Data Mapping Algorithm and Its Application to
Chance Discovery Process Support
Yasufumi Takama, Yoshihiro Iwase, Yuta Seo .......................167
Smart Discussion Environment on a Bulletin Board System
Wataru Sunayama ...............................................183
Part IV Cognitive
Prefiguring Ethical Chances: The Role of Moral Mediators
Lorenzo Magnani ................................................205
A NeuroCognitive Approach to Decision-Making in Chance
Discovery
W.L. Tung, C. Quek .............................................231
Part V Design and Marketing
Creative Marketing as Application of Chance Discovery
Yukio Ohsawa, Masaki Usui.......................................253
Extracting High Quality Scenario for Consensus on
Specifications of New Products
Kenichi Horie, Yukio Ohsawa .....................................273
Is there a Needle in the Haystack?
Renate Fruchter .................................................287
A Requirement Acquisition Process as an Evolved Chance
Discovery
Noriyuki Kushiro, Yukio Ohsawa...................................315
ConExSIR: A Dialogue-based Framework of Design Team
Thinking and Discovery
Ozgur Eris, David Bergner, Malte Jung, Larry Leifer .................329