61.
Inductive Logic Programming Beyond Logical Implication
- Jianguo Lu
This paper discusses the generalization of definite Horn programs beyond
the ordering of logical implication. Since the seminal paper on generalization
of clauses based on ` subsumption, there are various extensions in
this area. Especially in inductive logic programming(ILP), people are using
various methods that approximate logical implication, such as inverse
resolution(IR), relative least general generalization(RLGG), and inverse
implication(II), to generalize clauses. However, the logical implication is
not the most desirable form of generalization. A program is more general
than another program does not necessarily mean that the former should
logically imply the latter. Instead, a more natural notion of generalization
is the set inclusion ordering on the success set...
62.
Inductive Logic Programming Beyond Logical Implication
- Jianguo Lu
This paper discusses the generalization of definite Horn programs beyond
the ordering of logical implication. Since the seminal paper on generalization
of clauses based on ` subsumption, there are various extensions in
this area. Especially in inductive logic programming(ILP), people are using
various methods that approximate logical implication, such as inverse
resolution(IR), relative least general generalization(RLGG), and inverse
implication(II), to generalize clauses. However, the logical implication is
not the most desirable form of generalization. A program is more general
than another program does not necessarily mean that the former should
logically imply the latter. Instead, a more natural notion of generalization
is the set inclusion ordering on the success set...
63.
Contributions to Inductive Logic Programming
- Afstudeerscriptie Bestuurlijke Informatica,R. M. De Wolf
Contents
Preface iii
1 What is Inductive Logic Programming? 1
1.1 The importance of learning : : : : : : : : : : : : : : : : : : : : : 1
1.2 Inductive learning : : : : : : : : : : : : : : : : : : : : : : : : : : 2
1.3 The problem setting for ILP : : : : : : : : : : : : : : : : : : : : : 4
1.4 Other problem settings : : : : : : :...
64.
Inductive Logic Program Synthesis with DIALOGS
- Pierre Flener
DIALOGS (Dialog-based Inductive and Abductive LOGic program Synthesizer) is a schema-guided synthesizer
of recursive logic programs; it takes the initiative and minimally queries a (possibly computationally naive)
specifier for evidence in her/his conceptual language. The specifier must know the answers to such simple queries,
because otherwise s/he wouldn't even feel the need for the synthesized program. DIALOGS can be used by
any learner (including itself) that detects, or merely conjectures, the necessity of invention of a new predicate.
65.
GeLog - A System Combining Genetic Algorithm with Inductive Logic Programming
We have developed a genetic logic programming system (GeLog) which implements a combination of two different approaches for automatic programming: inductive logic programming and genetic algorithm.
66.
Composition of biases in Inductive Logic Programming
- Christel Vrain
. Studying biases is an important topic in Inductive Logic Programming,
and many works have dealt with the problem of dening interesting
formalisms for expressing biases. The expected properties for
such representation languages are usually declarativity, universality, easy
shift of biases and learning optimization. In this paper, we propose a new
formalism which was designed when attempting to apply learning techniques
to Data Mining applications. In that context, an important property
is incrementality, i.e., how to modify a set of already coded biases,
without re-coding the entire set of biases. The formalism we propose is
based on Regular Tree Grammars: they seem interesting in the purpose of
incrementality, because they...
67.
Inductive Logic Programming for Natural Language Processing
- Raymond J. Mooney
. This paper reviews our recent work on applying inductive
logic programming to the construction of natural language processing
systems. We have developed a system, Chill, that learns a parser from a
training corpus of parsed sentences by inducing heuristics that control an
initial overly-general shift-reduce parser. Chill learns syntactic parsers
as well as ones that translate English database queries directly into executable
logical form. The ATIS corpus of airline information queries was
used to test the acquisition of syntactic parsers, and Chill performed
competitively with recent statistical methods. English queries to a small
database on U.S. geography were used to test the acquisition of a complete
natural language interface,...
68.
Inductive Logic Programming for Natural Language Processing
- Raymond J. Mooney
. This paper reviews our recent work on applying inductive
logic programming to the construction of natural language processing
systems. We have developed a system, Chill, that learns a parser from a
training corpus of parsed sentences by inducing heuristics that control an
initial overly-general shift-reduce parser. Chill learns syntactic parsers
as well as ones that translate English database queries directly into executable
logical form. The ATIS corpus of airline information queries was
used to test the acquisition of syntactic parsers, and Chill performed
competitively with recent statistical methods. English queries to a small
database on U.S. geography were used to test the acquisition of a complete
natural language interface,...
69.
Recovering Software Specifications with Inductive Logic Programming
- William W. Cohen
We consider using machine learning techniques to
help understand a large software system. In particular,
we describe how learning techniques can
be used to reconstruct abstract Datalog specifications
of a certain type of database software from
examples of its operation. In a case study involving
a large (more than one million lines of C)
real-world software system, we demonstrate that
off-the-shelf inductive logic programming methods
can be successfully used for specification recovery;
specifically, Grendel2 can extract specifications for
about one-third of the modules in a test suite with
high rates of precision and recall. We then describe
two extensions to Grendel2 which improve
performance on this task: one which allows it to
output a set...
70.
Scientific Knowledge Discovery using Inductive Logic Programming
- Stephen Muggleton
This paper is an overview of scientific knowledge discovery tasks carried
out using Inductive Logic Programming (ILP). The results reviewed have
been published in some of the top general science journals, and as such are
among the strongest examples of semi-automated scientific discovery in
the Artificial Intelligence literature. Space restrictions do not permit this
paper to cover other discovery areas of ILP. These include the discovery of
linguistic features in natural language data and the discovery of patterns
in traffic data.
1 Introduction
The pharmaceutical industry is increasingly overwhelmed by large-volume-data.
This is generated both internally as a side-effect of screening tests and combinatorial
chemistry, as well as externally from sources...
71.
Scientific Knowledge Discovery using Inductive Logic Programming
- Stephen Muggleton
This paper is an overview of scientific knowledge discovery tasks carried
out using Inductive Logic Programming (ILP). The results reviewed have
been published in some of the top general science journals, and as such are
among the strongest examples of semi-automated scientific discovery in
the Artificial Intelligence literature. Space restrictions do not permit this
paper to cover other discovery areas of ILP. These include the discovery of
linguistic features in natural language data and the discovery of patterns
in traffic data.
1 Introduction
The pharmaceutical industry is increasingly overwhelmed by large-volume-data.
This is generated both internally as a side-effect of screening tests and combinatorial
chemistry, as well as externally from sources...
72.
Learning Trading Rules with Inductive Logic Programming
- Liviu Badea
. We apply Inductive Logic Programming (ILP) for inducing trading
rules formed out of combinations of technical indicators from historical market
data. To do this, we first identify ideal trading opportunities in the historical
data, and then feed these as examples to an ILP learner, which will try to induce
a description of them in terms of a given set of indicators. The main
contributions of this paper are twofold. Conceptually, we are learning strategies
in a chaotic domain in which learning a predictive model is impossible.
Technically, we show a way of dealing with disjunctive positive examples,
which create significant problems for most inductive learners.
1 Introduction and...
73.
Recovering Software Specifications with Inductive Logic Programming
- William W. Cohen
We consider using machine learning techniques to
help understand a large software system. In particular,
we describe how learning techniques can
be used to reconstruct abstract Datalog specifications
of a certain type of database software from
examples of its operation. In a case study involving
a large (more than one million lines of C)
real-world software system, we demonstrate that
off-the-shelf inductive logic programming methods
can be successfully used for specification recovery;
specifically, Grendel2 can extract specifications for
about one-third of the modules in a test suite with
high rates of precision and recall. We then describe
two extensions to Grendel2 which improve
performance on this task: one which allows it to
output a set...
74.
Incorporating Linguistics Constraints into Inductive Logic
- James Cussens,Stephen Pulman
We report work on eectively incorporating linguistic
knowledge into grammar induction. We
use a highly interactive bottom-up inductive
logic programming (ILP) algorithm to learn
`missing' grammar rules from an incomplete
grammar. Using linguistic constraints on, for
example, head features and gap threading, reduces
the search space to such an extent that,
in the small-scale experiments reported here,
we can generate and store all candidate grammar
rules together with information about their
coverage and linguistic properties. This allows
an appealingly simple and controlled method
for generating linguistically plausible grammar
rules. Starting from a base of highly speci
c rules, we apply least general generalisation
and inverse resolution to generate more general
rules. Induced rules are ordered, for example...
75.
Learning an Approximation to Inductive Logic
- Frank Dimaio,Jude Shavlik
One challenge faced by many Inductive Logic Programming (ILP)
systems is poor scalability to problems with large search spaces and many
examples. Randomized search methods such as stochastic clause selection
(SCS) and rapid random restarts (RRR) have proven somewhat successful at
addressing this weakness. However, on datasets where hypothesis evaluation is
computationally expensive, even these algorithms may take unreasonably long
to discover a good solution. We attempt to improve the performance of these
algorithms on datasets by learning an approximation to ILP hypothesis
evaluation. We generate a small set of hypotheses, uniformly sampled from
the space of candidate hypotheses, and evaluate this set on actual data. These
hypotheses and their...
76.
Inductive Logic Programming: Theory And Methods
- Stephen Muggleton,De Raedt
this paper, we provide an introduction to ILP. The introduction focusses on
what we believe to be the foundations of the field. This paper is not a bottom-up
paper based on describing small differences between many different systems. It is
instead a top-down synthetic overview of concepts, terminology and methods. We
are not overly concerned with discussing the implementation details of particular
systems and approaches because the differences are often quite minor and of not
great interest to a general audience. We aim instead at providing a conceptual
framework for presenting ILP at four levels of description: a semantic level (defining
the problem of ILP), a generic ILP...
77.
A Rapprochement Between Deductive and Inductive Logic
- Donald Gillies,Centre For Logic,Probability In Artificial
Traditionally logic was considered as having two branches: deductive and inductive. However the
development of the subject from Frege (1879) up to about 1970 brought about a divergence between
deductive and inductive logic. It is argued in this paper that developments in artificial intelligence
in the last twenty or so years (particularly logic programming and machine learning) have created a
new framework for logic in which deductive and inductive logic can, once again, be treated as similar
branches of the same discipline.
Keywords: deductive logic, inductive logic, logic programming, machine learning, PROLOG
1 Introduction
The aim of this paper is to explore some consequences for the philosophy of...
78.
The Origins of Inductive Logic Programming: A Prehistoric Tale
- Claude Sammut
This paper traces the development of the main ideas that have led to the
present state of knowledge in Inductive Logic Programming. The story
begins with research in psychology on the subject of human concept
learning. Results from this research influenced early efforts in Artificial
Intelligence which combined with the formal methods of inductive
inference to evolve into the present discipline of Inductive Logic
Programming.
INTRODUCTION
Inductive Logic Programming is often considered to be a young discipline.
However, it has its roots in research dating back nearly 40 years. This paper
traces the development of ideas beginning in psychology and the effect
they had on concept learning research in Artificial Intelligence.
Independent...
79.
Inductive Logic Programming for Data Mining
- Er Alves,Rui Camacho,Eugenio Oliveira
This paper addresses the problem of data mining in Inductive
Logic Programming (ILP) motivated by its application in the domain
of economics.
80.
Inductive Logic Program Synthesis with DIALOGS
- Pierre Flener
. dialogs (Dialogue-based Inductive and Abductive LOGic
program Synthesizer) is a schema-guided synthesizer of recursive logic
programs; it takes the initiative and queries a (possibly computationally
naive) specifier for evidence in her/his conceptual language. The specifier
must know the answers to such simple queries, because otherwise s/he
wouldn't even feel the need for the synthesized program. dialogs can be
used by any learner (including itself) that detects, or merely conjectures,
the necessity of invention of a new predicate. Due to its foundation on a
powerful codification of a "recursion-theory" (by means of the template
and constraints of a divide-and-conquer schema), dialogs needs very
little evidence and is very fast.
1 Introduction
This...
