2,822 research outputs found
The Difficulties of Learning Logic Programs with Cut
As real logic programmers normally use cut (!), an effective learning
procedure for logic programs should be able to deal with it. Because the cut
predicate has only a procedural meaning, clauses containing cut cannot be
learned using an extensional evaluation method, as is done in most learning
systems. On the other hand, searching a space of possible programs (instead of
a space of independent clauses) is unfeasible. An alternative solution is to
generate first a candidate base program which covers the positive examples, and
then make it consistent by inserting cut where appropriate. The problem of
learning programs with cut has not been investigated before and this seems to
be a natural and reasonable approach. We generalize this scheme and investigate
the difficulties that arise. Some of the major shortcomings are actually
caused, in general, by the need for intensional evaluation. As a conclusion,
the analysis of this paper suggests, on precise and technical grounds, that
learning cut is difficult, and current induction techniques should probably be
restricted to purely declarative logic languages.Comment: See http://www.jair.org/ for any accompanying file
Learning First-Order Definitions of Functions
First-order learning involves finding a clause-form definition of a relation
from examples of the relation and relevant background information. In this
paper, a particular first-order learning system is modified to customize it for
finding definitions of functional relations. This restriction leads to faster
learning times and, in some cases, to definitions that have higher predictive
accuracy. Other first-order learning systems might benefit from similar
specialization.Comment: See http://www.jair.org/ for any accompanying file
Low Size-Complexity Inductive Logic Programming: The East-West Challenge Considered as a Problem in Cost-Sensitive Classification
The Inductive Logic Programming community has considered
proof-complexity and model-complexity, but, until recently,
size-complexity has received little attention. Recently a
challenge was issued "to the international computing community"
to discover low size-complexity Prolog programs for classifying
trains. The challenge was based on a problem first proposed by
Ryszard Michalski, 20 years ago. We interpreted the challenge
as a problem in cost-sensitive classification and we applied a
recently developed cost-sensitive classifier to the competition.
Our algorithm was relatively successful (we won a prize). This
paper presents our algorithm and analyzes the results of the
competition
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