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Artificial Intelligence Communications 13(4) © IOS Press A note on the
utility of incremental learning

Christophe Giraud-Carrier
Department of Computer Science, University of Bristol Bristol BS8 1UB, UK,
[log in to unmask]
Historically, inductive machine learning has focused on non-incremental
learning tasks, i.e., where the training set can be constructed a priori
and learning stops once this set has been duly processed. There are,
however, a number of areas, such as agents, where learning tasks are
incremental. This paper defines the notion of incrementality for learning
tasks and algorithms. It then provides some motivation for incremental
learning and argues in favour of the design of incremental learning
algorithms for solving incremental learning tasks. A number of issues
raised by such systems are outlined and the incremental learner ILA is
used for illustration.

Artificial Intelligence Communications 13(4) © IOS Press Quantitative
Disjunctive Logic Programming: semantics and computation

Cristinel Mateis
Institut für Informationssysteme 184/2, Technical University of
Vienna, Favoritenstrasse 9-11, A-1040 Wien, Austria ,
[log in to unmask]
A new knowledge representation language, called QDLP, which extends DLP to
deal with uncertain values is introduced. Each (quantitative) rule is
assigned a certainty degree interval (a subinterval of [0,1]). The
propagation of uncertainty information from the premises to the conclusion
of a quantitative rule is achieved by means of triangular norms (T-norms).
Different T-norms induce different semantics for one given quantitative
program. In this sense, QDLP is parameterized and each choice of a T-norm
induces a different QDLP language. Each T-norm is eligible for events with
determinate relationships (e.g., independence, exclusiveness) between
them. Since there are infinitely many T-norms, it turns out that there is
a family of infinitely many QDLP languages. This family is carefully
studied and the set of QDLP languages which generalize traditional DLP is
precisely singled out. Algorithms for computing the minimal models of
quantitative programs are proposed.

Artificial Intelligence Communications 13(4) © IOS Press Complexity
results for restricted credulous default reasoning

Xishun Zhao
Corresponding author., DechengDing, HansKleine Büning
Department of Mathematics, Henan Normal University, Xinxiang 453002, P.R.
China , [log in to unmask], Department of Mathematics, Nanjing
University, Nanjing 210093, P.R. China E-mail: [log in to unmask],
Department of Mathematics and Computer Science, Paderborn University,
33095 Paderborn, Germany E-mail: [log in to unmask]
This paper concentrates on the complexity of the decision problem deciding
whether a literal belongs to at least one extension of a default theory
<D,W> in which D is a set of Horn defaults and W is a definite Horn
formula or a Bi-Horn formula.

Artificial Intelligence Communications 13(4) © IOS Press A framework to
deal with interference in connectionist systems

Vicente Ruiz de Angulo
Corresponding author., CarmeTorras
Institut de Robòtica i Informàtica Industrial, (CSIC-UPC), Edifici NEXUS,
Gran Capità 2-4, 08034-Barcelona, Spain ,
ruiz, [log in to unmask]
We analyze the conditions under which a memory system is prone to
interference between new and old items. Essentially, these are the
distributedness of the representation and the lack of retraining. Both
are, however, desirable features providing compactness and speed. Thus, a
two-stage framework to palliate interference in this type of systems is
proposed based on exploiting the information available at each moment. The
two stages are separated by the instant at which a new item becomes known:
(a) interference prevention, prior to that instant, consists in preparing
the system to minimize the impact of learning new items and (b)
retroactive interference minimization, posterior to that instant, seeks to
learn the new item while minimizing the damages inflicted on the old
items. The subproblems addressed at the two stages are stated rigorously
and possible methods to solve each of them are presented.

Artificial Intelligence Communications 13(4) © IOS Press Relational
learning vs. propositionalization
Investigations in inductive logic programming and propositional machine
learning

Stefan Kramer
Present address: Institute for Computer Science, Albert-Ludwigs-University
Freiburg, Georges-Köhler-Allee, Geb. 079, D-79110 Freiburg i. Br.,
Germany. This work was partly supported by the Austrian Fonds zur
Förderung der Wissenschaftlichen Forschung (FWF) under grant P12645 INF.
The Austrian Research Institute for Artificial Intelligence is supported
by the Austrian Federal Ministry for Transport, Innovation and Technology.

Austrian Research Institute for Artificial Intelligence, Schottengasse 3,
A-1010 Vienna, Austria , [log in to unmask]

Artificial Intelligence Communications 13(4) © IOS Press Bayesian system
for student modeling

EvaMillán Valldeperas
E.T.S.I. Informática, Universidad de Málaga, Apdo. 4114, Málaga 29080,
Spain , [log in to unmask]

Artificial Intelligence Communications 13(4) © IOS Press Scalable Search
in Computer Chess - Algorithmic Enhancements and Experiments at High
Search Depths
Ernst A. Heinz, Vieweg, 2000

HermannKaindl
Mödling, Austria

Artificial Intelligence Communications 13(4) © IOS Press Calendar
2001