Representation issues in reasoning and learning

Area Meeting of CompulogNet Area "Computational Logic and Machine Learning"

Prague, 20 September 1997

in conjunction with the Seventh International Workshop on Inductive Logic Programming ILP-97

Call for contributions


Background

Logical formalisms typically allow many degrees of freedom when it comes to representing real-world knowledge. For instance, one may represent functions in the domain of discourse either by function symbols or by predicates; one may include semantic equality in the language, or be happy with unification; one may insist that predicates and constants are ontologically meaningful, in the sense that they refer to properties, relations and objects in the domain of discourse, but one may also be more liberal by admitting expressions such as "colour(X,red)", in which the property of redness is represented by a constant rather than a monadic predicate.

Such choices can be approached from different perspectives. For instance, a philosopher would probably choose the first alternative in each of the issues just mentioned; a logician interested in common-sense reasoning would look for non-truthfunctional alternatives to material implication; a computer scientist would be more concerned with efficiency of algorithms processing the formalism, and would find reasons to eschew the use of semantic equality; and a machine learner might choose a particular representation scheme simply because it suits the tabular form of her data, or because it allows to express the induced knowledge in a form comprehensible for the domain expert.


Focus of the Area Meeting

This CompulogNet Area Meeting concentrates on the practial aspects of representation formalisms as employed in Computational Logic and Machine Learning. In Machine Learning the main criterion to choose a particular scheme for representing data or hypotheses is pragmatic: does it lead to useful results? In Computational Logic the choice for particular reasoning schemes has mostly been guided by the efficiency of proof procedures and the applicability to real-world reasoning tasks. In both cases, a fully developed methodology for choosing a particular representation scheme is lacking. Furthermore, the relationship between the choices made by computational logicians and machine learners is unclear. The intention of the Area Meeting is to discuss these and related questions, with an emphasis on the following issues:

CHOOSING THE RIGHT REPRESENTATION SCHEME FOR LEARNING

COMPARING REPRESENTATIONS FOR LEARNING AND REASONING

BEYOND CLASSICAL LOGIC FOR LEARNING

Do we need:


Contributions and format of the Area Meeting

Participants are required to submit a short position paper (2-4 pages) addressing at least one of the meeting's central issues, identified above. Electronic submission of postscript files to Peter.Flach@kub.nl is strongly preferred. Contributed position papers will be made available on the Web before the meeting. Authors of selected contributions will be asked to give a short presentation. In addition, there will be several invited contributions. There will be ample time for discussion.


Timetable


Organizers

Peter Flach          Nada Lavrac 
Tilburg University   J. Stefan Institute 
P.O.Box 90153        Jamova 39 
5000 LE Tilburg      1000 Ljubljana 
the Netherlands      Slovenia
Peter.Flach@kub.nl   Nada.Lavrac@ijs.si

Last change: March 27, 1997 / Peter Flach
WWW location: http://macpf.kub.nl:2080/AreaMeeting97/CFP.html