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Guest editorial
Mach Learn
Rui Camacho 0 1 2
Ross King 0 1 2
Ashwin Srinivasan 0 1 2
0 A. Srinivasan IBM India Research Laboratory Indian Institute of Technology New Delhi , India
1 R. King Department of Computer Science University of Wales , Aberystwyth Wales , United Kingdom
2 R. Camacho ( ) LIACC/FEUP University of Porto , Portugal
Springer
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The papers in this issue are snapshots of ILP at the age of fourteen. Adolescence, as we all
remember, was a time when change was normal and we knew everything. We are reminded
here of Mark Twain’s recollection: “When I was a boy of fourteen, my father was so ignorant
I could hardly stand to have the old man around. But when I got to be twenty-one, I was
astonished at how much he had learned in seven years”. And so it is with ILP. Visiting friends
and relatives would be surprised at the changes: some of which is captured in the papers
here. In them we see a face of ILP in which systematic search for rules has been replaced
by randomised ones; first-order trees have been replaced by random forests of first-order
trees; observations no longer refer to the target concept being learnt; theories are constructed
using very large ensembles of clauses, and agent programs are constructed from observing
an expert. Compare this against the ILP that constructed a small number of definite clauses,
each with about 4 literals, using a data set of no more than a few hundred examples of the
target concept. And, as with Twain, we can expect to be astonished over the next few years.
As the character of ILP develops over the next few years, there are some traits that
are intriguing, and some that require a pause for thought. Amongst the former is a trend
towards the combination of statistical and relational information. We expect this will allow
ILP to be applied across a much wider range of problems than previously attempted. Also
interesting is the use of ILP as a general-purpose technique for constructing new features.
This has repeatedly been shown to be a very good way to use an ILP system. There are two
characteristics which may be viewed as a cause for concern by some. First, comprehensibility
of theories constructed appears to be no longer a feature of all ILP-constructed theories. This
Fig. 1 A rough categorisation of ILP papers published from 1991. These are estimates are obtained from a
manual classification by one of us (A.S.) of the ILPNet2 bibliographic entries. The average values over the
period are: 38 (Conceptual), 9 (Implementation) and 17 (Application)
is specially so with ILP systems capable of constructing and using very large numbers of
clauses, or combining probabilistic and logical information in complex ways. Second, there
appears to be a trend towards fewer conceptual and application-oriented papers (see Fig. 1).
If these estimates are reflective of reality, then the problem needs addressing. We are pleased
to see that the ILP application presented in this issue has some substantially different features
to many in the recent past and it could take the place of the “mutagenesis” problem as the
new Drosophila for ILP.
Now for the clerical details. This volume contains five papers based on original ones
presented at the 14th International Conference on Inductive Logic Programming. All the
papers were significantly re-worked and extended with respect to their original version that
appear in the ILP 2004 Proceedings. ILP 2004 was held in Porto, Portugal, in September
2004. This annual meeting of ILP continues to be the premier forum for presenting the
most recent and exciting work in the field. Six invited talks—three from fields outside ILP,
but nevertheless highly relevant to it—and 20 full presentations formed the nucleus of the
conference.
Van Assche et al. show how the concept of random forests can improve relational decision
trees. They address and propose solutions for the problem of introducing aggregate functions.
The resulting first order random forest induction algorithm was implemented and
experimentally evaluated on a variety of datasets. The results indicate that first order random forests
with complex aggregates are an efficient and effective approach towards learning relational
classifiers that involve aggregates over complex selections.
Developing efficient methods to search the hypothesis space continues to be an important
issue in ILP. Zˇ elezn y´ et al. demonstrate the benefits of randomising and restarting ILP search
procedure. They show that significant speedups can be obtained using randomised restarts
by comparing five search algorithms operating on two principally different classes of ILP
problems.
In the paper by Tamaddoni-Nezhad et al. a problem that is increasingly important in
biological applications is addressed. Observations do not necessarily refer to the target predicate
and background knowledge cannot be assumed to be complete. Using a combination of
abduction and induction an ILP program is used to construct a model by completing defin (...truncated)