81.
Multiple Predicate Learning in Two Inductive Logic Programming Settings
- Nada Lavra
Inductive logic programming (ILP) is a research area which has its roots in inductive machine learning
and computational logic. The paper gives an introduction to this area based on a distinction between
two different semantics used in inductive logic programming, and illustrates their application in
knowledge discovery and programming. Whereas most research in inductive logic programming has
focussed on learning single predicates from given datasets using the normal ILP semantics (e.g. the
well known ILP systems GOLEM and FOIL), the paper investigates also the non-monotonic ILP
semantics and the learning problems involving multiple predicates. The non-monotonic ILP setting
avoids the order dependency problem of the normal setting...
82.
Multiple Predicate Learning in Two Inductive Logic Programming Settings
- Nada Lavra
Inductive logic programming (ILP) is a research area which has its roots in inductive machine learning
and computational logic. The paper gives an introduction to this area based on a distinction between
two different semantics used in inductive logic programming, and illustrates their application in
knowledge discovery and programming. Whereas most research in inductive logic programming has
focussed on learning single predicates from given datasets using the normal ILP semantics (e.g. the
well known ILP systems GOLEM and FOIL), the paper investigates also the non-monotonic ILP
semantics and the learning problems involving multiple predicates. The non-monotonic ILP setting
avoids the order dependency problem of the normal setting...
83.
Logic Programming and Co-Inductive Definitions
This paper focuses on the assignment of meaning to infinite derivations in logic programming. Several approaches have been developped by considering infinite elements in the universe of the discourse but none are complete. By considering proofs as objects in a co-inductive set, standard properties of co-inductive definitions are used both to explain this incompleteness and to define a sound and complete semantics, based on the "logic program as co-inductive definition" paradigm, for a subclass of infinite derivations, called infinite derivations over a finite domain (i.e. derivations which do not compute infinite terms).
84.
SFOIL: Stochastic Approach to Inductive Logic Programming
- Uros Pompe,Matevz Kovacic,Igor Kononenko
Current systems in the field of Inductive Logic
Programming (ILP) use, primarily for the sake
of efficiency, heuristically guided search techniques.
Such greedy algorithms suffer from local optimization
problem. Present paper describes a system named
SFOIL, that tries to alleviate this problem by using
a stochastic search method, based on a generalization
of simulated annealing, called Markovian neural network.
Various tests were performed on benchmark,
and real-world domains. The results show both, advantages
and weaknesses of stochastic approach.
1 Introduction
The main problem, when dealing with induction of
logic programs from instances, is an enormous number
of possible logic descriptions. Inductive Logic
Programming systems perform the task by searching
the state space of logic programs. This...
85.
SFOIL: Stochastic Approach to Inductive Logic Programming
- Uros Pompe,Matevz Kovacic,Igor Kononenko
Current systems in the field of Inductive Logic
Programming (ILP) use, primarily for the sake
of efficiency, heuristically guided search techniques.
Such greedy algorithms suffer from local optimization
problem. Present paper describes a system named
SFOIL, that tries to alleviate this problem by using
a stochastic search method, based on a generalization
of simulated annealing, called Markovian neural network.
Various tests were performed on benchmark,
and real-world domains. The results show both, advantages
and weaknesses of stochastic approach.
1 Introduction
The main problem, when dealing with induction of
logic programs from instances, is an enormous number
of possible logic descriptions. Inductive Logic
Programming systems perform the task by searching
the state space of logic programs. This...
86.
LPMEME: A Statistical Method for Inductive Logic Programming
- Karan Bhatia,Charles Elkan
. This paper describes LPMEME, a new learning algorithm
for inductive logic programming that uses statistical techniques to find
first-order patterns. LPMEME takes as input examples in the form of
logical facts and outputs a first-order theory that is represented to some
degree in all of the examples. LPMEME uses an underlying statistical
model whose parameters are learned using expectation maximization,
an iterative gradient descent method for maximum likelihood parameter
estimation. The underlying statistical model is described and the EM
algorithm developed. Experimental tests show that LPMEME can learn
first-order concepts and can be used to find approximate solutions to the
subgraph isomorphism problem.
Keywords: learning, inductive logic programming, maximum likelihood...
87.
Logic Programming and Co-Inductive Denitions
This paper focuses on the assignment of meaning to innite derivations
in logic programming. Several approaches have been developped
by considering innite elements in the universe of the discourse but
none are complete. By considering proofs as objects in a co-inductive
set, standard properties of co-inductive denitions are used both to
explain this incompleteness and to dene a sound and complete semantics,
based on the logic program as co-inductive denition paradigm,
for a subclass of innite derivations, called innite derivations over a
nite domain (i.e. derivations which do not compute innite terms).
Programmation logique et dnitions
co-inductives
Rsum
Les SLD-drivations innies sont tudies en identiant un programme
dni avec un ensemble de dnitions...
88.
Cautious Induction in Inductive Logic Programming
- Simon Anthony,Alan M. Frisch
. Many top-down Inductive Logic Programming systems use a
greedy, covering approach to construct hypotheses. This paper presents
an alternative, cautious approach, known as cautious induction. We conjecture
that cautious induction can allow better hypotheses to be found,
with respect to some hypothesis quality criteria. This conjecture is supported
by the presentation of an algorithm called CILS, and with a complexity
analysis and empirical comparison of CILS with the Progol system.
The results are encouraging and demonstrate the applicability of
cautious induction to problems with noisy datasets, and to problems
which require large, complex hypotheses to be learnt.
1 Introduction
Within the Inductive Logic Programming (ILP) paradigm [4, 2], many of...
89.
Cautious Induction in Inductive Logic Programming
- Simon Anthony,Alan M. Frisch
. Many top-down Inductive Logic Programming systems use a
greedy, covering approach to construct hypotheses. This paper presents
an alternative, cautious approach, known as cautious induction. We conjecture
that cautious induction can allow better hypotheses to be found,
with respect to some hypothesis quality criteria. This conjecture is supported
by the presentation of an algorithm called CILS, and with a complexity
analysis and empirical comparison of CILS with the Progol system.
The results are encouraging and demonstrate the applicability of
cautious induction to problems with noisy datasets, and to problems
which require large, complex hypotheses to be learnt.
1 Introduction
Within the Inductive Logic Programming (ILP) paradigm [4, 2], many of...
90.
LPMEME: A Statistical Method for Inductive Logic Programming
- Karan Bhatia,Charles Elkan
This paper describes LPMEME, a learning algorithm for inductive logic
programming that uses statistical techniques to find first order patterns. LPMEME
takes as input examples in the form of logical statements and outputs
a first order theory that is represented to some degree in all of the examples.
LPMEME uses an underlying statistical model whose parameters are learned
using expectation maximization, an iterative gradient descent method for
maximum likelihood parameter estimation. The underlying statistical model
is described and the EM algorithm developed. Experimental tests show that
LPMEME can learn first order concepts and can be used to find approximate
solutions to the subgraph isomorphism problem.
Keywords: learning, inductive logic programming,...
91.
Data Mining: From Statistics to Inductive Logic Programming
Many different approaches exist in the field of
data mining, for instance using inductive logic
programming or statistics. In this paper we
combine these two paradigms, applying them
to the following type of classification problem
in data mining. Given is a database of insurance
clients. We can consider it as a sample
of the population (clients and potential clients).
Our task is to partition the population into homogeneous
classes w.r.t. causing car-accidents.
A homogeneous class is a set of people that
can not be divided into subclasses with different
risks of causing accidents. A definite program
clause can be used to define a class in
the sample and hence also a class in the...
92.
Application of Inductive Logic Programming for Learning ECG Waveforms
. In this paper a learning system is presented which integrates
an ECG waveform classifier (called PECG) with an interactive learner
(called IMPUT). The PECG system is based on an attribute grammar
specification of ECGs that has been transformed to Prolog. The IMPUT
system combines the interactive debugging technique IDT with the unfolding
algorithm introduced in SPECTRE. Using the IMPUT system
we can effectively assist in preparing the correct description of the basic
structures of ECG waveforms. The application of the system for learning
ECG waveforms is demonstrated with the help of an example.
4
Keywords: inductive logic programming, program specialization, syntactic
pattern recognition
1 Introduction
In this paper a complex system is...
93.
Application of Inductive Logic Programming for Learning ECG Waveforms
. In this paper a learning system is presented which integrates
an ECG waveform classifier (called PECG) with an interactive learner
(called IMPUT). The PECG system is based on an attribute grammar
specification of ECGs that has been transformed to Prolog. The IMPUT
system combines the interactive debugging technique IDT with the unfolding
algorithm introduced in SPECTRE. Using the IMPUT system
we can effectively assist in preparing the correct description of the basic
structures of ECG waveforms. The application of the system for learning
ECG waveforms is demonstrated with the help of an example.
4
Keywords: inductive logic programming, program specialization, syntactic
pattern recognition
1 Introduction
In this paper a complex system is...
94.
Application of Inductive Logic Programming for Learning ECG Waveforms
. In this paper a learning system is presented which integrates
an ECG waveform classifier (called PECG) with an interactive learner
(called IMPUT). The PECG system is based on an attribute grammar
specification of ECGs that has been transformed to Prolog. The IMPUT
system combines the interactive debugging technique IDT with the unfolding
algorithm introduced in SPECTRE. Using the IMPUT system
we can effectively assist in preparing the correct description of the basic
structures of ECG waveforms.
4
Keywords: inductive logic programming, program specialization, syntactic
pattern recognition
1 Introduction
In this paper a complex system is presented that is able to classify ECG waveforms
described as a combination of primitives. The system helps...
95.
An efficient validation mechanism for Inductive Logic Programming using compositionality
- Arnaud Lallouet; Lionel Martin
Inductive Logic Programming, which consists in learning clauses from examples, can be viewed as a cycle conception/validation leading to the acceptance of the induced program provided that it fulfills a certain criterion. We focus on the validation step in the context of empirical multi-predicate learning of normal clauses. Thanks to a compositional semantics, the classical validation step of the complete induced program can be replaced by the verification of local properties for a cut out into units, considerably limiting the usual combinatorial explosion. Moreover, we provide a semantics-preservative transformation which allows to simplify the program and provides a further refinement...
96.
Biochemical knowledge discovery using Inductive Logic Programming
- Stephen Muggleton,Ashwin Srinivasan,R. D. King
. Machine Learning algorithms are being increasingly used for
knowledge discovery tasks. Approaches can be broadly divided by distinguishing
discovery of procedural from that of declarative knowledge.
Client requirements determine which of these is appropriate. This paper
discusses an experimental application of machine learning in an area
related to drug design. The bottleneck here is in finding appropriate
constraints to reduce the large number of candidate molecules to be
synthesised and tested. Such constraints can be viewed as declarative
specifications of the structural elements necessary for high medicinal activity
and low toxicity. The first-order representation used within Inductive
Logic Programming (ILP) provides an appropriate description
language for such constraints. Within this...
97.
Database Mining through Inductive Logic Programming
- Himanshu Gupta,Iain Mclaren,Alfred Vella
Rapid growth in the automation of business
transactions has lead to an explosion in the size of
databases. It has been realised for a long time that the
data in these databases contains hidden information
which needs to be extracted. Data mining is a step in
this direction and aims to find potentially useful and
non-trivial information from these databases in the
form of patterns. As the size and complexity of these
database increases, the question that normally arises
is "Are the existing data mining techniques efficient
enough for large databases"? This paper addresses
this issue and looks at an alternative approach,
Inductive Logic Programming, and its integration
with deductive databases. This integration...
98.
Inductive Logic Programming: derivations, successes and shortcomings
- Stephen Muggleton,Ox Qd
Inductive Logic Programming (ILP) is a research area which investigates
the construction of first-order definite clause theories from examples
and background knowledge. ILP systems have been applied successfully in
a number of real-world domains. These include the learning of structureactivity
rules for drug design, finite-element mesh design rules, rules for
primary-secondary prediction of protein structure and fault diagnosis rules
for satellites. There is a well established tradition of learning-in-the-limit
results in ILP. Recently some results within Valiant's PAC-learning framework
have also been demonstrated for ILP systems. In this paper it is argued
that algorithms can be directly derived from the formal specifications
of ILP. This provides a common basis for...
99.
Inductive Logic Programming with Well-Modedness Constraints
- Andreas Hamfelt,Jrgen Fischer Nilsson
This paper presents an approach to induction of logic programs from examples
using a problem decomposition and reduction approach. This is in contrast to the
prevailing logic program induction paradigm which relies on generalization of programs
from examples. Our induction scheme applies a combinatory form of logic
programs which is conducive to a top-down inductive synthesis process by its compositional
nature. The induction process is subjected to various constraints, notably
well-modedness constraints, which ensure synthesis of well-moded procedurally acceptable
programs.
Keywords: logical combinators, recursion combinators, synthesis by composition
and specialization, inductive synthesis, program schemata.
1 Introduction
Inductive Logic Programming (ILP) is concerned with the construction of logic programs
from sample program results...
100.
Incorporating Linguistics Constraints into Inductive Logic Programming
- 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...
