Knowledge Acquisition: Approaches, Algorithms and Applications: Pacific Rim Knowledge Acquisition Workshop, PKAW 2008, Hanoi, Vietnam, December 15-16, 2008, Revised Selected Papers

251 Pages·2009·12.93 MB·English

by  Yann Chevaleyre

Lecture Notes in Artificial Intelligence 5465
EditedbyR.Goebel,J.Siekmann,andW.Wahlster
Subseries of Lecture Notes in Computer Science
Debbie Richards Byeong-Ho Kang (Eds.)
Knowledge Acquisition:
Approaches, Algorithms
and Applications
Pacific Rim KnowledgeAcquisitionWorkshop, PKAW 2008
Hanoi,Vietnam, December 15-16, 2008
Revised Selected Papers
1 3
SeriesEditors
RandyGoebel,UniversityofAlberta,Edmonton,Canada
JörgSiekmann,UniversityofSaarland,Saarbrücken,Germany
WolfgangWahlster,DFKIandUniversityofSaarland,Saarbrücken,Germany
VolumeEditors
DebbieRichards
MacquarieUniversity,ComputingDepartment
DivisionofInformationandCommunicationSciences
Sydney,NSW,2109,Australia
E-mail:[email protected]
Byeong-HoKang
UniversityofTasmania
SchoolofComputingandInformationSystems
Launceston,TAS7250,Australia
E-mail:[email protected]
LibraryofCongressControlNumber:Appliedfor
CRSubjectClassification(1998):I.2.6,I.2,H.2.8,H.3-5,F.2.2,C.2.4,K.3
LNCSSublibrary:SL7–ArtificialIntelligence
ISSN 0302-9743
ISBN-10 3-642-01714-2SpringerBerlinHeidelbergNewYork
ISBN-13 978-3-642-01714-8SpringerBerlinHeidelbergNewYork
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Preface
Withthegrowingrecognitionofthepivotalrolethatknowledgeplaysinthesus-
tainability,competitivenessandgrowthofindividuals,organizationsandsociety,
finding solutions to address the knowledge acquisition bottleneck is even more
important today than in the early stages of this field. The knowledge acquisi-
tion community is interested in topics spanning from the fundamental views on
knowledgethataffecttheknowledgeacquisitionprocessandtheuseofknowledge
in knowledge engineering to the evaluation of knowledge acquisitiontechniques,
tools and methods.
Asafieldwithinthelargerfieldofartificialintelligence(AI),solutionsincor-
porating other areas of AI such as ontologicalengineering, agent-basedtechnol-
ogy, robotics, image recognition and the Semantic Web are common, as are
knowledge acquisition methods related to other fields of computing such as
databases,the Internet,informationretrieval,languagetechnology,softwareen-
gineering, decision support systems and game technology. Many solutions are
application focused addressing real-world problems such as knowledge mainte-
nance and validation, reuse and sharing, merging and reconciliation within a
wide range of problem domains.
The Pacific Knowledge Acquisition Workshops (PKAW) have provided a fo-
rum for the past two decades for researchers and practitioners in the Pacific
region and beyond who work in the field of knowledge acquisition. PKAW cov-
ers a spectrum of techniques and approaches ranging from manual knowledge
acquisitionfromahumanexperttofullyautomatedknowledgeacquisitionusing
machine-learning or data-mining methods.
ThisvolumeseekstodisseminatethelatestsolutionsfromthePacificKnowl-
edgeAcquisitionWorkshop2008(PKAW2008)heldinHanoi,TaiwanduringDe-
cember15-16,2008inconjunctionwiththePacificRimInternationalConference
onArtificialIntelligence (PRICAI 2008).The workshopreceived57submissions
from14countries.Fromthese,weaccepted15papers(26%)forfullpresentation
andanother14forshortpresentation.Allpaperswereblindreviewedbyatleast
three members of the Program Committee. This volume contains a selection of
these papersfurther revisedfollowingworkshopdiscussions.The papersdemon-
strate a balance of theoretical, technical and application-driven research, many
papersincorporatingallthreefoci.Approximatelyhalfthepapersreflectthe in-
creasinguseofWeb-baseddataforknowledgediscoveryandmanagement.
The Workshop Co-chairs would like to thank all those who were involved in
PKAW 2008 including the PRICAI 2008 Organizing Committee, PKAW Pro-
gramCommitteemembers,thosewhosubmittedpapersandreviewedthemand
VI Preface
ofcoursetheauthors,presentersandattendees.Wewarmlyinviteyoutopartic-
ipate in PKAW2010anticipated to be held in Seoul,Koreain conjunction with
PRICAI 2010.
March 2009 Debbie Richards
Byeong Ho Kang
Organization
Honorary Chairs
Paul Compton University of New South Wales, Australia
Hiroshi Motoda OsakaUniversityandAFOSR/AOARD,Japan
Program Co-Chairs
Debbie Richards Macquarie University, Australia
Byeong Ho Kang University of Tasmania, Australia
Program Committee
Ghassan Beydoun University of Wollongong, Australia
Bob Colomb University of Technology Malaysia, Malaysia
Paul Compton University of New South Wales, Australia
Richard Dazeley University of Ballarat, Australia
Fabrice Guillet LINA - Polytech’Nantes, France
Udo Hahn Jena University, Germany
Byeong Ho Kang University of Tasmania, Australia
Rob Kremer University of Calgary, Canada
Maria Lee Shih Chien University, Taiwan
Tim Menzies LCSEE, WVU, USA
Toshiro Minami Kyushu University, Japan
Hiroshi Motoda OsakaUniversityandAFOSR/AOARD,Japan
Kozo Ohara Osaka University, Japan
Takashi Okada Kwansei Gakuin University, Japan
Son Pham College of Technology, VNU, Vietnam
Frank Puppe University of Wu¨rzburg, Germany
Ulrich Reimer University of Applied Sciences St. Gallen,
Switzerland
Debbie Richards Macquarie University, Australia
Hendra Suryanto University of New South Wales, Australia
Seiji Yamada National Institute of Informatics, Japan
Kenichi Yoshida University of Tsukuba, Japan
Tetsuya Yoshida Hokkaido Univerisity, Japan
VIII Organization
Additional Reviewers
Daniel Bidulock
Jason Heard
Uwe Heck
Claudia Marinica
Christian Thiel
Ryan Yee
Table of Contents
Machine Learning and Data Mining
Experiments with Adaptive Transfer Rate in Reinforcement Learning... 1
Yann Chevaleyre, Aydano Machado Pamponet, and
Jean-Daniel Zucker
Clustering over Evolving Data Streams Based on Online Recent-Biased
Approximation .................................................. 12
Wei Fan, Yusuke Koyanagi, Koichi Asakura, and Toyohide Watanabe
Automatic Database Creation and Object’s Model Learning ........... 27
Nguyen Dang Binh and Thuy Thi Nguyen
Finding the Most Interesting Association Rules by Aggregating
Objective Interestingness Measures................................. 40
Tri Thanh Nguyen Le, Hiep Xuan Huynh, and Fabrice Guillet
Pruning Strategies Based on the Upper Bound of Information Gain for
Discriminative Subgraph Mining ................................... 50
Kouzou Ohara, Masahiro Hara, Kiyoto Takabayashi,
Hiroshi Motoda, and Takashi Washio
A Novel Classification Algorithm Based on Association Rules Mining ... 61
Bay Vo and Bac Le
Incremental Knowledge Acquisition
Multiple Classification Ripple Round Rules: A Preliminary Study ...... 76
Ivan Bindoff, Tristan Ling, and Byeong-Ho Kang
Generalising Symbolic Knowledge in Online Classification and
Prediction ...................................................... 91
Richard Dazeley and Byeong-Ho Kang
Web-Based Techniques and Application
Using Formal Concept Analysis towards Cooperative E-Learning....... 109
Ghassan Beydoun
Navigation and Annotation with Formal Concept Analysis (Extended
Abstract) ....................................................... 118
Peter Eklund and Jon Ducrou
X Table of Contents
What Does an Information Diffusion Model Tell about Social Network
Structure? ...................................................... 122
Takayasu Fushimi, Takashi Kawazoe, Kazumi Saito,
Masahiro Kimura, and Hiroshi Motoda
Web Mining for Malaysia’s Political Social Networks Using Artificial
Immune System ................................................. 137
Ahmad Nadzri Muhammad Nasir, Ali Selamat, and
Md. Hafiz Selamat
Accessing Touristic Knowledge Bases through a Natural Language
Interface........................................................ 147
Juana Mar´ıa Ruiz-Mart´ınez, Dagoberto Castellanos-Nieves,
Rafael Valencia-Garc´ıa, Jesualdo Toma´s Ferna´ndez-Breis,
Francisco Garc´ıa-Sa´nchez, Pedro Jos´e Vivancos-Vicente,
Juan Salvador Castej´on-Garrido, Juan Bosco Camo´n, and
Rodrigo Mart´ınez-B´ejar
ItemSpider: Social Networking Service That Extracts Personal
Character from Individual’s Book Information ....................... 161
Tetsuya Tsukamoto, Hiroyuki Nishiyama, and Hayato Ohwada
Acquiring Marketing Knowledge from Internet Bulletin Boards ........ 173
Hiroshi Uehara and Kenichi Yoshida
Domain Specific Knowledge Acquisition Methods
and Applications
A Design for Library Marketing System and Its Possible
Applications..................................................... 183
Toshiro Minami
Knowledge Audit on Special Children Communities .................. 198
Aida Suzana Sukiam, Azizah Abdul Rahman, and
Wardah Zainal Abidin
A Dance Synthesis System Using Motion Capture Data............... 208
Kenichi Takahashi and Hiroaki Ueda
Discovering Areas of Expertise from Publication Data ................ 218
Meredith Taylor and Debbie Richards
Facial Feature Extraction Using Geometric Feature and Independent
Component Analysis ............................................. 231
Toan Thanh Do and Thai Hoang Le
Author Index.................................................. 243
Experiments with Adaptive Transfer Rate in
Reinforcement Learning
Yann Chevaleyre1, Aydano Machado Pamponet2, and Jean-Daniel Zucker3
1 Universit Paris-Dauphine
[email protected]
2 Universit Paris 6
[email protected]
3 IRD URGodes
Centre IRD del’Ile deFrance, Bondy,France
[email protected]
Abstract. Transfer algorithms allow the use of knowledge previously
learned on related tasks to speed-up learning of the current task. Re-
cently, many complex reinforcement learning problems have been suc-
cessfully solved by efficient transfer learners. However, most of these
algorithmssufferfromasevereflaw:theyareimplicitlytunedtotransfer
knowledge between tasks having a given degree of similarity. In other
words, if the previous task is very dissimilar (resp. nearly identical) to
the current task, then the transfer process might slow down the learn-
ing(resp.mightbefarfromoptimalspeed-up).Inthispaper,weaddress
thisspecificissuebyexplicitlyoptimizingthetransferratebetweentasks
and answer to the question : “can the transfer rate be accurately opti-
mized, and at what cost ?”. We show that this optimization problem
is related to the continuum bandit problem. We then propose a generic
adaptivetransfermethod(AdaTran),whichallows toextendseveralex-
istingtransfer learningalgorithms tooptimize thetransferrate.Finally,
we run several experimentsvalidating our approach.
1 Introduction
In the reinforcement learning problem, an agent acts in an unknown environ-
ment, with the goal of maximizing its reward. All learning agents have to face
the exploration-exploitation dilemma: whether to act so as to explore unknown
areas or to act consistently with experience to maximize reward(exploit). Most
research on reinforcement learning deal with this issue. Recently Strehl et al.
[17]showedthatnear optimalstrategiescouldbe reachedin as few asO(cid:2)(S×A)
time steps. However, in many real-world learning problems, the state space or
the action space have an exponential size.
One way to circumvent this problem is to use previously acquired knowledge
related to the current task being learned. This knowledge may then be used to
guideexplorationthroughthestate-actionspace,hopefullyleadingtheagentto-
wardsareasinwhichhighrewardscanbefound.Thisknowledgecanbeacquired
in different ways:
D.RichardsandB.-H.Kang(Eds.):PKAW2008,LNAI5465,pp.1–11,2009.
(cid:2)c Springer-VerlagBerlinHeidelberg2009