Sixth Workshop on
Learning with Logics
Program 
July 6th (Mon)  

13:00  Opening 
13:1015:10  Session 1 Takashi Yamada, Hitoshi Yamasaki, Takayoshi Shoudai A Polynomial Time Algorithm for Finding a Minimally Generalized Externally Extensible Outerplanar Graph Pattern Taku Aratsu, Kouichi Hirata, Tetsuji Kuboyama Local Frequency Distances for Rooted Ordered Trees Yuichi Kameda and Hiroo Tokunaga Inferability of Unbounded Unions of Certain Closed Set Systems Mahito Sugiyama, Eiju Hirowatari, Hideki Tsuiki, Akihiro Yamamoto Learning Figures with the Hausdorff Metric by Selfsimilar Sets 
15:3016:30  Invited Talk 1 Thomas Zeugmann The Normalized Information Distance and its Applications Using Graph Cuts 
16:5018:20  Session 2 Fumiya Nakagaito, Tomonobu Ozaki, Takenao Ohkawa Discovery of Quantitative Sequential Patterns from Event Sequences Takashi Katoh, Hiroki Arimura, Koichi Hirata Mining Frequent $k$Partite Episodes from Event Sequences Haruya Iwasaki, Elsa Loekito, Shinichi Minato, James Bailey Comparison of ZDDVectors and WZDDs for Frequent Pattern Mining 
July 7th (Tue) 

10:0011:00  Session 3 Kimihito Ito, Thomas Zeugmann and Yu Zhu Clustering the Normalized Compression Distance for Virus Data Joe Suzuki A Conjecture on Strongly Consistent Learning 
11:2012:20  Invited Talk 2 Marta Arias A Canonical Representation for Propositional Horn Theories and its Relation to Query Learning 
13:2014:50  Session 4 Ken Satoh Computing Minimal Models by Minimal DNF formula Yoshitaka Yamamoto, Katsumi Inoue, Koji Iwanuma Hypothesis Enumeration by CFinduction Seishi Ouchi and Akihiro Yamamoto Learning from Positive Data based on the MINL Strategy with Refinement Operators 
14:5015:00  Closing 
18:00  Joint Banquet with DMSS 
Scope of the
Workshop
Logic is one of the mathematical methods of
representing data as well as rules in Machine Learning and Knowledge
Discovery. Recently some methods are developed based on algebraic
concepts, e.g. closed sets, for the aim. In the converse, Machine Learning
procedures are found to provide procedural semantics to algebraic, and
logical inference.
The aim of LLLL is to bring together
researchers who are interested in both of the areas of machine learning
and computational logic, and to have intensive discussions on various
relations between the two with making their interchange more active. The
LLLL workshop was started as a domestic workshop in 2002. As an
international workshop, we held LLLL at Kitakyushu in 2005, at Tokyo in
2006, and at Miyazaki in 2007. LLLL2009 is supported by SIGFPAI. LLLL2009
is collocated with DMSS2009, which is held July 78 at the same site. The
CFP of DMSS2009 is available at
http://ibisml.org/dmss2009/
Potential
(but not exclusive) topics include :
Registration fee:
Free, but joining Banquet costs 6,000 JPY.
Inivited Speakers
Marta Arias Vicente (Universitat Politècnica de Catalunya, Spain)
A Canonical Representation for Propositional Horn Theories and its Relation
to Query Learning
Abstract: In this talk I will describe a canonical representation for propositional
Horn theories known as the GuiguesDuquenne basis (or GD basis), that was
introduced in the field of Formal Concept Analysis. I will show strong
connections between this representation and two topics in query learning
theory. First, I will show that a wellknown algorithm by Angluin, Frazier
and Pitt that learns Horn CNF always outputs the GD basis independently
of the counterexamples it receives; second, and if time permits, I will
show how to construct strong polynomial certificates from the GD basis
directly. This is joint work with JosEL. Balcázar.
Thomas Zeugmann (Hokkaido University)
The Normalized Information Distance and its Applications Using Graph Cuts
Abstract: First we recall some background necessary to understand the definition
of the Normalized Information Distance. Then we outline as possibleapproximations
the Normalized Compression Distance and the Google Distance. Next, we turn
our attention to clustering. Clustering algorithms working with a matrix
of pairwise similarities (kernel matrix) for the data are widely known
and used, a particularly popular class being spectral clustering algorithms.
In contrast, working directly with the pairwise distance matrix has found
less attention despite the fact that in many applications distance matrices
are often directly given. So, we look at clustering algorithms based on
Semidefinite Programming (SDP) based on the work of Frieze and Jerrum and
on spectral clustering working over similarity matrices. Finally, we shortly
propose a simple heuristic for dealing with missing data, i.e., the case
where some of the pairwise distances or similarities are not known.
Workshop
Banquet
Workshop Banquet is held with DMSS at 18:00 on
July 7th. Attendees who would like to join the banquet must send an email
with the followings in the body:
 your name,
 affiliation,
and
 email
Banquet fee is 6,000 JPY (JPY cash only).
How to
Submit
LLLL2009 accepts extended abstracts written
in English only. The abstracts are distributed in the workshop sites only
and not considered to be formal publications. The extended abstract should
be 18 pages in the format at the address:
http://www.dumbo.ai.kyutech.ac.jp/LLLL/jsaiws_template.lzh
The authors should send their submitted papers as a PDF file to
akihiro@i.kyotou.ac.jp,
hirata@ai.kyutech.ac.jp, and
minato@ist.hokudai.ac.jp
(all).
Please write the text `[LLLL09:
Paper Submission]' in the subject of the massage, and put the
following in the body:
 Title of your paper,
 A list of
all authors and their affiliations (please add "*"
mark to the presenter),
 Name, Telephone number, and
Email address of the corresponding author.
Important dates  

June 8, 2009 ==> June 11, 2009 

June 19, 2009 ==> June 22, 2009 

July 67, 2009  Workshop 
KyodaiKaikan Tel: +81757518311 Fax: +81757615403 URL : http://www.kyodaikaikan.jp/ Access:  From Kyoto JR Station, take the bus 206 at terminal D2, stop at the bus stop Kyodaiseimonmae and walk about 5 mins.  By Keihan train, get off at Keihan Marutamachi Station and walk about 7 mins. 
Committee
Members
Workshop Chair
Akihiro
Yamamoto (Kyoto University)
PC Cochairs
Kouichi Hirata (Kyushu Institute ofTechnology)
Shinichi
Minato (Hokkaido University)
Program Commitee
(current status)
Yoji Akama
(Tohoku University)
Marta Arias (Universitat
Politecnica de Catalunya)
Kouichi Hirata (Kyushu Institute ofTechnology)
Tamas Horvath
(University of Bonn and Fraunhofer
IAIS)
Katsumi Inoue (National Institute of
Infomatics)
Shinichi Minato (Hokkaido
University)
Tetsuhiro Miyahara (Hiroshima
City University)
Taisuke Sato (Tokyo
Institute ofTechnology)
Takayoshi Shoudai (Kyushu University)
Hiroo Tokunaga (Tokyo Metropolitan University)
Gyorgy Turan (University of Illinois)
Akihiro Yamamoto (Kyoto University)
Website and Contact
http://www.iip.ist.i.kyotou.ac.jp/LLLL09/
Postal
addess : Akihiro Yamamoto