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ILP Newsletter
Volume 2, Number 1, 13th January 1995
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Address all communication related to the ILP Newsletter to ilpnet@ijs.si
To subscribe/unsubscribe send email with subject SUBSCRIBE/UNSUBSCRIBE ILPNEWS
Send contributions in messages with subject heading ILPNEWS CONTRIBUTION
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available via the World Wide Web (WWW), URL http://www-ai.ijs.si/ilpnet.html
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Contents:
- Book announcement: Concept Formation and Knowledge Revision
- Book announcement: Logic Program Synthesis from Incomplete Information
- Preliminary book announcement:
Inductive Logic Programming-from Machine Learning to Software Engineering
- Report: Fourth International ILP Workshop (ILP'94)
- Call for papers: Fifth International ILP Workshop (ILP'95) - ASCII
- Call for papers: Fifth International ILP Workshop (ILP'95) - LaTeX
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===============================================================
NEW BOOK by Kluwer Academic Publishers
===============================================================
Concept Formation and Knowledge Revision
Stefan Wrobel, GMD
===============================================================
Regarding concepts as the elementary representational
vocabulary of an intelligent system, this book focuses on
representation change as a concept formation task. Taking
an interdisciplinary approach from psychological foundations
to computer implementations, Concept Formation and Knowledge
Revision draws on existing psychological results about the
nature of human concepts and concept formation. The book shows
that computational concept formation can usefully be understood
as a demand-driven process triggered by the representational
needs of the learning system, and that knowledge revision is a
suitable context for such a process.
In using a first-order representation, this book is part
of the rapidly developing field of Inductive Logic Programming
(ILP). It presents a detailed analysis of the revision problem
for first-order clausal theories, describes suitable knowledge
revision and concept formation operators, and demonstrates
their usefulness both theoretically and empirically within the
learning knowledge acquisition system MOBAL.
By integrating computational issues with psychological and
fundamental discussions of concept formation phenomena, the
book will be of interest to a wide spectrum of readers, whether
theoretically, cognitively or practically inclined.
From the foreword by Katharina Morik:
"Machine learning - as artificial intelligence
in general - grows from three different roots:
cognition, theory, and applications. ... The
ideal to combine the three sources of artificial
intelligence research has almost never been reached.
... The most important capability for artificial
intelligence is to keep the integrative view and
to create a true original work that goes beyond
the collection of pieces from different fields.
In presenting the long way from psychological
investigations to an implemented system and its
theoretical foundation, this book achieves such an
integrative view of concept formation and knowledge
revision."
Contents:
Foreword.
1. Introduction.
2. The Psychology of Concepts and Concept Formation.
3. Concept Representation in a Paraconsistent Logic with Higher-
Order Elements.
4. Knowledge Revision as a Concept Formation Context.
5. Demand-Driven Concept Formation.
6. Embeddedness.
7. Conclusion.
A. MOBAL Software Info Page. B. Glossary of Symbols.
References. Index.
Kluwer Academic Publishers, Dordrecht, Boston, London. 256 pp.
Hardbound ISBN: 0-7923-9500-X.
Prices: NLG: 160.00, USD: 79.95, GBP: 57.75.
Limited number of discounted copies available directly from the
author at DEM 59.95 (inside Germany), DEM 89.95/USD 59.95
(elsewhere).
For order forms and further information, check out
http://nathan.gmd.de/persons/stefan.wrobel/kluwerbook.html
on the WWW, or send E-Mail to wrobel@gmd.de.
===============================================================
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BOOK ANNOUNCEMENT
Logic Program Synthesis from Incomplete Information
by
Pierre Flener
Bilkent University, Ankara, Turkey
THE KLUWER INTERNATIONAL SERIES IN ENGINEERING AND COMPUTER SCIENCE 295
Kluwer Academic Publishers, Boston, November 1994
264 pages Hardbound ISBN 0-7923-9532-8 85USD 60.50GBP 165NLG
Program synthesis is a solution to the software crisis. If we had a program
that develops correct programs from specifications, then program validation
and maintenance would disappear from the software life-cycle, and one could
focus on the more creative tasks of specification elaboration, validation,
and maintenance, because replay of program development would be less costly.
This monograph describes a novel approach to Inductive Logic Programming
(ILP), which cross-fertilizes logic programming and machine learning.
Aiming at the synthesis of recursive logic programs only, and this from
incomplete information, we take a software engineering approach that is
more appropriate than a pure artificial intelligence approach.
This book is suitable as a secondary text for graduate level courses in
software engineering and artificial intelligence, and as a reference for
practitioners of program synthesis.
Contents
Foreword (by Alan W. Biermann)
Preface
I: State of the Art
1. Automatic Programming
2. Deductive Inference in Automatic Programming
3. Inductive Inference in Automatic Programming
4. A Logic Program Development Methodology
5. Objectives
II: Building Blocks
6. A Specification Approach
7. A Framework for Stepwise Logic Algorithm Synthesis
8. Algorithm Analysis and Algorithm Schemata
9. The Proofs-as-Programs Method
10. The Most-Specific-Generalization Method
III: A Logic Algorithm Synthesis Mechanism
11. Overview of the Synthesis Mechanism
12. The Expansion Phase
13. The Reduction Phase
14. Conclusion
Appendix: Conventions, Abbreviations, and Symbols
References
Subject Index
Keywords:
Computer and Information Science: Programming Languages/Operating Systems
Computer and Information Science: Software Engineering
Computer and Information Science: AI Languages
Computer and Information Science: Machine Learning
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
ORDER FORM
Author: Pierre Flener
Title : Logic Program Synthesis from Incomplete Information
PB/HB : Hardbound
ISBN : 0-7923-9532-8 Price: USD 85.00, NLG 165.00, GBP 60.50
Ref: KAPIS
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PRELIMINARY BOOK ANNOUNCEMENT
Inductive Logic Programming: from Machine Learning to Software Engineering
by
Francesco Bergadano and Daniele Gunetti
MIT Press
The book is expecially oriented towards Softare Engineering applications of ILP,
but also contains an extended survey of basic research done in ILP and of more
recent developments, as well as an introduction to the field.
The book will be available in the second half of 1995.
For more information contact Daniele Gunetti: gunetti@di.unito.it
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Report on the 4th International Workshop on
Inductive Logic Programming (ILP94)
by Giovanni Semeraro
Workshop Chair: Stefan Wrobel (GMD, Germany)
ILP94 was held in Bad Honnef/Bonn (Germany) from September 12th
to September 14th 1994. Local support was provided by GMD. ILP94
was sponsored by GI (Gesellschaft fur Informatik e. V. - German
Society for Computer Science), GMD (Gesellschaft fur Mathematik
und Datenverarbeitung MBH) and MLnet (ESPRIT Network of Excellence
in Machine Learning). The workshop was attended by 60
participants; about 16 papers were accepted as full papers and
presented in the plenary session, and 11 further papers were
selected for poster presentation. Each day of the workshop started
with an invited talk.
Katharina Morik gave a talk entitled "The Art of ILP
Applications", in which three real-world applications of ILP
algorithms were presented, namely classification of students
choices about their place of residence, security management in a
telecommunication environment, and robot navigation. These
examples were selected as they show that real-world applications
of machine learning require several kinds of problems to be
solved. Moreover, they show the wide range of applicability of
machine learning.
Lorenza Saitta ("ILP: An Alternative View"), starting from an in-
depth analysis of several definitions of ILP in the literature,
helped to put ILP in the wider perspective of machine learning, by
pointing out differences and similarities with other machine
learning areas.
Paul Vitanyi ("Inductive Reasoning") presented a general theory of
inductive reasoning, based on a form of Bayes rule with no prior
probabilities. A practical application of such a theory is
represented by the Minimum Description Length (MDL) principle
(Rissanen, 83). Furthermore, it can be proven that the MDL
principle selects typical hypotheses, where typical means most
likely for some computable prior probability. Such a proof relies
on a method of inference based on Kolmogorov Complexity
(Solomonoff, 64), which can, in turn, be traced to Occams Razor
and Churchs Thesis. The universal p-randomness test (Martin-Loef)
is used to validate this theory. Finally, the speaker showed an
example of application of this theory to the problem of inferring
a function from input/output examples.
Full papers can be roughly divided into four areas, namely:
applications,
ILP algorithms/systems,
computational learning theory, and
theory revision.
The first area includes the first two papers in the following
overview. In the presentation by A. Srinivasan, S. Muggleton, R.D.
King, and M.J.E. Sternberg ("Mutagenesis: ILP Experiments in a
Non-Determinate Biological Domain"), the new ILP system PROGOL was
used to discover rules for mutagenicity in nitroaromatic
compounds. From a biological point of view, this problem is
relevant because highly mutagenic nitroaromatics have been found
to be carcinogenic and often cause damage to DNA. From an ILP
point of view, this problem is interesting since it involves a
highly non-determinate relational representation that cannot be
dealt with by all the ILP systems which incorporate the ij-
determinate restriction. PROGOLs main features are: possibility
of using non-ground background knowledge (and in the more general
form of horn clauses rather than unit clauses), mode-directed
inverse resolution, best-first search strategy. Volker Klingspor
("GRDT: Enhancing Model-Based Learning for its Application in
Robot Navigation") presented a new system, called GRDT (Grammar
Based Rule Discovery Tool), that proved effective to solve the
task of learning concepts for navigation of autonomous mobile
robots. GRDT is mainly an extension of RDT (Kietz and Wrobel, 92),
and overcomes some shortcomings that FOIL (Quinlan, 90) and
GRENDEL (Cohen, 93) showed when used to solve the same task.
The majority of ILP algorithms/systems, can be subdivided into
methods for learning logic programs and methods for learning
concept descriptions in a logical form, where the boundary between
them is represented by recursion (allowing recursive rules or
not). A. Hamfelt and J.F. Nilsson ("Inductive Metalogic
Programming") proposed a method and a testbed for induction of a
class of logic programs. The method exploits higher order cliches
to restrict the hypothesis language to predefined program
recursion schemes. F. Bergadano and D. Gunetti ("Learning Clauses
by Tracing Derivations") presented TRACY, a system that learns
logic programs from examples. TRACY learns a logic program as a
whole rather than learning a single clause at each learning step.
In other words, the hypothesis space of TRACY is the space of
logic programs, that is, the power set of the space of possible
clauses. The search performed by TRACY is based on an intensional
evaluation of learned clauses and makes use of backtracking to
choose an alternative derivation when some negative example can be
derived from the learned logic program. The algorithm is proved to
be correct and sufficient and it does not depend on the kind and
number of training examples. Matevz Kovacic ("MILP - A Stochastic
Approach to Inductive Logic Programming") presented the system
MILP, which replaces a greedy search technique, common to many ILP
system, with stochastic search. Moreover, MILPs evaluation
function makes use of the MDL principle in order to avoid
overfitting and ranks the remaining hypotheses according to the
classification accuracy on the training set. Tests in the domains
of king-rook-king chess end-games and the finite element mesh
design showed that MILP significantly outperforms other ILP
algorithms. Werner Emde ("Inductive Learning of Characteristic
Concept Descriptions") presented an improved version of his system
COLA (Emde, 94), called COLA-2. COLA-2 adopts a novel approach to
the problem of learning characteristic descriptions from examples.
Such an approach is also applicable when a small set of classified
examples is available, while most of the training examples are
unclassified. COLA-2 embodies a conceptual clustering algorithm,
called SPRITE, to take advantage of the unclassified observations.
Michelle Sebag and Celine Rouveirol ("Induction of Maximally
General Clauses Consistent with Integrity Constraints") presented
an extension to definite clauses of previous works in
propositional logic (Sebag, 94a) and in a restriction of first
order logic (Sebag, 94b). The aim of this work is to show that
only near-miss examples are useful to build the set of maximally
general hypotheses which are consistent with the available
negative examples. Jorg-Uwe Kietz and Marcus Lubbe ("An Efficient
Subsumption Algorithm for Inductive Logic Programming") addressed
a central problem for ILP, namely the efficiency of theta-
subsumption. Generally speaking, the test for establishing whether
a clause d theta-subsumes a clause c is np-complete even if we
restrict ourselves to linked horn clauses and fix the number of
literals in c to a small constant. Thus, the authors show that two
restrictions on the hypothesis space, namely determinacy (of d wrt
c) and k-local clauses, can be used to make theta-subsumption
tractable. The corresponding two theta-subsumption algorithms
constitute the foundations of a future efficient bottom-up
algorithm for learning (the lgg of two any) determinate k-local
horn clauses. Furthermore, the reduction algorithm (under theta-
subsumption) can be greatly improved by these approaches. S.
Muggleton and C.D. Page jr. ("Self-Saturation of Definite
Clauses") investigated the development of complete algorithms for
lgg computation. Indeed, most of these algorithms invert theta-
subsumption and not implication, thus they are incomplete. This
incompleteness has been precisely characterized (Gottlob, 87).
Some fundamental questions concerning the existence and the
computability of lgg are related to the notion of inversion of
implication. Based on these questions, the paper introduces and
analyses the concepts of self-saturation and direct root self-
saturation of definite clauses and of arbitrary clauses. The main
result is that a finite lgg under implication of any two clauses
exists and is efficiently computable if the two clauses have
finite self-saturation. I. Stahl and I. Weber ("The Arguments of
Newly Invented Predicates in ILP") addressed the problem of
searching for the appropriate arguments of a newly invented
predicate. Predicate invention is largely used in ILP in order to
extend the hypothesis language (and consequently the hypothesis
space) when the original language is not rich enough for the
learning task.
Two papers can be ascribed to the area of computational learning
theory. S. Muggleton and C.D. Page jr. ("A Learnability Model for
Universal Representation") proposed a new computational model of
inductive learning, called u-learnability (universal
learnability). Such a model extends existing models by allowing
time-bounded concepts and probability distribution over
hypotheses. The emphasis is placed upon distribution rather than
representation. Luc de Raedt and Saso Dzeroski ("First Order JK-
Clausal Theories are PAC-Learnable") presented positive PAC-
learning results for the nonmonotonic ILP setting. Specifically,
first order range-restricted clausal theories, whose clauses have
up to k literals of size at most j, are polynomial-sample
polynomial-time PAC-learnable from positive examples only.
In the area of theory revision, the paper by H. Bostrom and P.
Idestam- Almquist ("Specialization of Logic Programs by Pruning
SLD-Trees for Definite Clauses") presents a specializing operator
based on unfolding and clause removal. J. Paakki, T. Gyimothy and
T. Horvath ("Effective Algorithmic Debugging for Inductive Logic
Programming") improved Shapiros algorithmic debugging of logic
programs (1983) by means of a technique which integrates category
partition testing and static program slicing. The improvement
consists in a reduction of the number of questions to the oracle.
Such a reduction is achieved by avoiding questions to the oracle
when a verification of the results of a procedure call can be
inferred from the test database. Furthermore, only the relevant
program execution paths that may have affected the value of an
incorrect output are analysed. P.R.J. Van der Laag and S.H.
Nienhuys-Cheng ("A Note on Ideal Refinement Operators in Inductive
Logic Programming") provided sufficient conditions for
nonexistence of ideal refinement operators (ideal means locally
finite, complete and proper) and showed that such conditions are
met when the hypothesis space is the set of Horn clauses and the
model of generalization is theta-subsumption or logical
implication. Therefore, ideal refinement operators for both of
these quasi-orderings do not exist.
Outside the tentative classification of the ILP94 papers, the
contribution by P. Flach ("Inductive Logic Programming and
Philosophy of Science") investigated the relations between work in
ILP and similar work in philosophy of science on the logical
characterization of scientific theory formation.
The proceedings of ILP-94 have been published as a GMD Technical
Report and are still available upon request from:
Ms. Ulrike Teuber
GMD, FIT.KI, Schloss Birlinghoven
53754 Sankt Augustin 1
E-Mail ulrike.teuber@gmd.de
As long as supplies last, they are free of charge.
Finally, I would like to thank Stefan Wrobel and Christine Harms
for the perfect organization of the workshop, for the pleasant
trip on the Rhine and in particular for the pioneering
introduction of a very interesting special session on "Rhine
Valley Wine Tasting".
Bibliography
(Cohen, 93) Cohen, W.W., Rapid Prototyping of ILP Systems Using
Explicit Bias. Proceedings Oo the IJCAI Workshop on ILP, 1993.
(Emde, 94) Emde, W., Inductive Learning of Characteristic Concept
Descriptions from Small Sets of Classified Examples. In F.
Bergadano and L. De Raedt (Eds.), Machine Learning: ECML-94,
Proceedings of the European Conference on Machine Learning,
Lecture Notes in Artificial Intelligence, 103-121, Springer-
Verlag, 1994.
(Gottlob, 87) Gottlob, G., Subsumption and Implication,
Information Processing Letters, 24:109-111, 1987.
(Kietz And Wrobel, 92) Kietz, J.U., and Wrobel, S., Controlling
the Complexity of Learning Through Syntactic and Task-Oriented
Models. In S. Muggleton (Ed.), Inductive Logic Programming, 107-
126, Academic Press, 1992.
(Quinlan, 90) Quinlan, J.R., Learning Logical Definitions from
Relations, Machine Learning, 5(3):239-266, 1990.
(Rissanen, 83) Rissanen, J., A Universal Prior for Integers and
Estimation by Minimum Description Length, Annals of Statistics,
11(1):416-431, 1983.
(Sebag, 94a) Sebag, M., Using Constraints to Building Version
Spaces. In F. Bergadano and L. De Raedt (Eds.), Machine Learning:
ECML-94, Proceedings of the European Conference on Machine
Learning, Lecture Notes in Artificial Intelligence, Springer-
Verlag, 1994.
(Sebag, 94b) Sebag, M., A Constraint-Based Induction Algorithm in
Fol, In Proceedings of IML94: International Conference on Machine
Learning, Morgan Kaufmann, 1994.
(Shapiro, 83) Shapiro, E., Algorithmic Program Debugging, Mit
Press, 1983.
(Solomonoff, 64) Solomonoff, R.J., A Formal Theory of Inductive
Inference, Information and Control, 7:376-388, 1964.
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5th International Workshop on Inductive Logic Programming
4-6 September 1995, Leuven, Belgium
Preliminary Announcement and Call for Papers
General Information :
ILP-95 is the 5th annual meeting of researchers in
and practitioners of inductive learning in first order
logic. Previous meetings have been organized in Viana de
Castello (91), Tokyo (92), Bled (93), and Bad Honnef (94).
Program :
The scientific program will include invited talks,
presentations of selected papers, poster and demo
sessions. The program will be complemented by and
overlap with an area meeting on Knowledge Representation
and Reasoning of the ESPRIT Network of Excellence
Compulog from 6-8 September 1995. It is our intention
to have Wednesday 6 September recognized as a joint
workshop of the ESPRIT Networks of Excellence in
Computational Logic (Compulog) and Machine Learning (MLnet).
Further information will become available in January.
Submission of Papers :
ILP solicits papers addressing inductive machine
learning within the representation offered by
computational logic. ILP-95 especially wishes to encourage
submissions of interest to both researchers in computational
logic and inductive machine learning. This includes (but is
not limited to) topics such as inductive synthesis of logic
programs, applications of abductive logic programming to
induction, meta-programming approaches to induction,
applications of inductive logic programming to software
engineering, deductive databases, database design, theory
revision, etc.
The proceedings will be distributed at the workshop,
and will appear as a technical report of the
K.U.Leuven, Computer Science Department. There are also
plans for publishing a post-conference volume with a major
publishing company.
Full papers are limited to 5000 words. Submissions should be
made in 5 copies to:
Luc De Raedt (ILP-95)
Department of Computer Science, Katholieke Universiteit Leuven
Celestijnenlaan 200A, B-3001 Heverlee (Belgium)
Whenever possible, authors should send their title page using
email to ilp95@cs.kuleuven.ac.be.
Important Dates :
Submission deadline 1 May 1995
Notification of acceptance/rejection 1 July 1995
Camera ready copy 1 August 1995
Program Chair :
Luc De Raedt (Katholieke Universiteit Leuven, Belgium).
Program Committee :
F. Bergadano (Italy) I. Bratko (Slovenia)
W. Cohen (USA) S. D~zeroski (Slovenia)
P. Flach (The Netherlands) P. Idestam-Almquist (Sweden)
N. Lavra~c (Slovenia) S. Matwin (Canada)
R. Mooney (USA) S. Muggleton (U.K.)
M. Numao (Japan) D. Page (U.K.)
J.R. Quinlan (Australia) A. Srinivasan (U.K.)
C. Rouveirol (France) C. Sammut (Australia)
S. Wrobel (Germany)
To receive further information about ILP-95 :
send email to ilp95@cs.kuleuven.ac.be,
or see (via WWW) http://www.cs.kuleuven.ac.be/~ilp95/.
%------------------------------------------------------------------------------%
%------------------------------------------------------------------------------%
% ILP'95 CFP - LaTeX
\documentstyle[12pt]{article}
\setlength{\textheight}{10.500in}
\setlength{\textwidth}{7.2in}
\oddsidemargin -0.5in
\topmargin -1in
\footheight 4mm
\footskip 4mm
\pagestyle{empty}
\begin{document}
\begin{center}
\renewcommand{\baselinestretch}{1.2}
\vspace{1cm}
{\bf \large ILP-95}\\
\medskip
{\bf 5th International Workshop on Inductive Logic Programming}
\ \\
\medskip
4-6 September 1995, Leuven, Belgium\ \\
\medskip
{\bf Preliminary Announcement and Call for Papers}
\end{center}
\begin{description}
\item[General Information]:\linebreak
ILP-95 is the 5th annual meeting of
researchers in and practitioners of inductive learning in
first order logic. Previous meetings have been
organized in Viana de Castello (91), Tokyo (92), Bled (93), and
Bad Honnef (94).
\item[Program]:\linebreak
The scientific program will include
invited talks, presentations of selected papers,
poster and demo sessions.
The program will be complemented by and overlap with an area meeting
on Knowledge Representation and Reasoning of the
ESPRIT Network of Excellence Compulog from 6-8 September 1995.
It is our intention to have Wednesday 6 September recognized
as a joint workshop of the ESPRIT Networks of Excellence
in Computational Logic (Compulog) and Machine Learning (MLnet).
Further information will become available in January.
\item[Submission of Papers]:\linebreak
ILP solicits papers addressing inductive machine learning
within the representation offered by computational logic.
ILP-95 especially wishes to encourage submissions
of interest to both researchers in computational logic
and inductive machine learning. This includes (but is not limited to)
topics such as inductive synthesis of logic programs,
applications of abductive logic programming to induction,
meta-programming approaches to induction,
applications of inductive logic programming to software engineering, deductive databases, database design, theory revision, etc.
The proceedings will be distributed at the workshop, and will appear
as a technical report of the K.U.Leuven, Computer Science Department.
There are also plans for publishing a post-conference volume
with a major publishing company.
Full papers are limited to 5000 words.
Submissions should
be made in 5 copies to:
\medskip
Luc De Raedt (ILP-95)\\
Department of Computer Science, Katholieke Universiteit Leuven\\
Celestijnenlaan 200A, B-3001 Heverlee (Belgium)
\medskip
Whenever possible, authors should send their title page using email to
ilp95@cs.kuleuven.ac.be.
\item[Important Dates]:\linebreak
Submission deadline \hspace*{2.0in} 1 May 1995\\
Notification of acceptance/rejection \hspace*{0.9in} 1 July 1995\\
Camera ready copy \hspace*{2.06in} 1 August 1995
\item[Program Chair]:\linebreak
Luc De Raedt (Katholieke Universiteit Leuven, Belgium).
\item[Program Committee]:\linebreak
\begin{tabular}{l l l}
F. Bergadano (Italy) & I. Bratko (Slovenia) & W. Cohen (USA) \\
S. D\v zeroski (Slovenia) & P. Flach (The Netherlands) & P. Idestam-Almquist (Sweden) \\
N. Lavra\v c (Slovenia) & S. Matwin (Canada) & R. Mooney (USA) \\
S. Muggleton (U.K.) & M. Numao (Japan) & D. Page (U.K.) \\
J.R. Quinlan (Australia) & A. Srinivasan (U.K.) & C. Rouveirol (France) \\
C. Sammut (Australia) & S. Wrobel (Germany) \\
\end{tabular}
%\item[Organizing Committee]:\linebreak
%H. Ad\'e, G. Sablon, L. Dehaspe, D. De Schreye, L. De Raedt, W. Van Laer.
\end{description}
\medskip
To receive further information about ILP-95, send email to
ilp95@cs.kuleuven.ac.be,\\
or see (via WWW) http://www.cs.kuleuven.ac.be/$\sim$ilp95/.
\end{document}
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*********************************************************
****** 8th EUROPEAN CONFERENCE ON MACHINE LEARNING ******
****** 25--27 April 1995, Heraklion, Crete, Greece ******
****** List of accepted papers and posters ******
*********************************************************
The list of accepted papers and posters, INCLUDING
ALL ABSTRACTS, is available from the ECML-95 WWW home page
ftp://ftp.gmd.de/ml-archive/general/ecml-95/ecml95.html
This page also gives access to ECML-95 registration
information. For further questions about the program,
contact ecml-95@gmd.de, for questions about registration,
contact ecml-95@ics.forth.gr.
For those without access to WWW, please find the list of
papers (w/o abstracts) below.
*** Accepted as full papers with plenary presentation
Learning Abstract Planning Cases
Ralph Bergmann, Wolfgang Wilke
The Role of Prototypicality in Exemplar-Based Learning
Yoram Biberman
Specialization of Recursive Predicates
Henrik Bostro:m
A Distributed Genetic Algorithm Improving the Generalization
Behavior of Neural Networks
Ju:rgen Branke, Udo Kohlmorgen, Hartmut Schmeck
Learning Non-Monotonic Logic Programs: Learning Exceptions
Yannis Dimopoulos, Antonis Kakas
A Comparitive Utility Analysis of Case-Based Reasoning and
Control-Rule Learning Systems
Anthony Francis, Ashwin Ram
A Minimization Approach to Propositional Inductive Learning
Dragan Gamberger
On Concept Space and Hypothesis Space in Case-Based Learning
Algorithms
Anthony D. Griffiths, Derek G. Bridge
The Power of Decision Tables
Ron Kohavi
Pruning Multivariate Decision Trees by Hyperplane Merging
Miroslav Kubat, Doris Flotzinger
Multiple-Knowledge Representation in Concept Learning
Thierry Van de Merckt, Christine Decaestecker
The Effect of Numeric Features on the Scalability of Inductive
learning Programs
Georgios Paliouras, David S. Bre'e
Analogical logic program synthesis form examples
Ken Sadohara, Makoto Haraguchi
A Guided Tour Through Hypothesis Spaces in ILP
Birgit Tausend
*** Accepted as posters with short overview presentation ***
JIGSAW: puzzling together RUTH and SPECTRE
Hilde Ade', Henrik Bostro:m
Discovery of Constraints and Data Dependencies in Datatbases
Siegfried Bell, Peter Brockhausen
Learning Disjonctive Normal Forms in a Classifier System
Philippe Collard, Cathy Escazut
The Effects of Noise on Efficient Incremental Induction
Gerard Conroy, David Dutton
Analysis of Rachmaninoff's Piano Performances Using Inductive
Logic Programming
Matthew J. Dovey
Handling real numbers in Inductive Logic Programming: a step
towards better behavioural clones
Saso Dzeroski, Ljupco Todorovski, Tanja Urbancic
Simplifying Decision Trees by Pruning and Grafting: New Results
Floriana Esposito, Donato Malerba, Giovanni Semeraro
A Tight Integration of Pruning and Learning
Johannes Fu:rnkranz
Decision-Tree Based Neural Network
Irena Ivanova, Miroslav Kubat
Learning Recursion with Iterative Bootstrap Induction
Ali'pio Jorge, Pavel Brazdil
Patching Proofs for Reuse
Thomas Kolbe, Christoph Walther
Adapting to Drift in Continuous Domains
Miroslav Kubat, Gerhard Widmer
Parallel Recombinative Reinforcement Learning
Aristidis Likas, Konstantinos Blekas, Andreas Stafylopatis
Learning to solve complex tasks for reactive systems
Mario Martin, Ulises Corte's
Co-operative Reinforcement Learning By Payoff Filters
Sadayoshi Mikami, Yukinori Kakazu, Terence Fogarty
Automatic Synthesis of Control Programs by Combination of
Learning and Problem Solving Methods
Wolfgang Mu:ller, Fritz Wysotzki
Analytical Learning Guided by Empirical Technology: An
Approach to Integration
Nikolay Nikolaev, E. Smirnov
A New MDL Measure for Robust Rule Induction
Bernhard Pfahringer
Class-Driven Statistical Discretization of Continuous Attributes
Marco Richeldi, Mauro Rossotto
Generating Neural Networks Through the Induction of Threshold
Logic Unit Trees
Mehran Sahami
Learning Classification Rules Using Lattices
Mehran Sahami
Hybrid Classification: Using Axis-Parallel and Oblique
Subdivisions of the Attribute Space
Barbara Schulmeister, Mario Bleich
An Induction-based Control for Genetic Algorithms
Miche!le Sebag, Marc Schoenauer, Caroline Ravise'
FENDER: An approach to theory restructuring
Edgar Sommer
Language Series Revisited: The Complexity of Hypothesis
Spaces in ILP
Irene Weber, Birgit Tausend, Irene Stahl
Prototype, Nearest Neighbor and Hybrid Algorithms for
Time Series Classification
Christel Wisotzki, Fritz Wysotzki
*** end of list ***
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