Introducing possibilistic logic in ILP for dealing with exceptions

In this paper we propose a new formalization of the inductive logic programming (ILP) problem for a better handling of exceptions. It is now encoded in first-order possibilistic logic. This allows us to handle exceptions by means of prioritized rules, thus taking lessons from non-monotonic reasoning...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Artificial intelligence Jg. 171; H. 16; S. 939 - 950
Hauptverfasser: Serrurier, Mathieu, Prade, Henri
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.11.2007
Elsevier
Schlagworte:
Applied sciences
Artificial intelligence
Computer Science
Computer science; control theory; systems
Exact sciences and technology
Inductive logic programming
Learning and adaptive systems
Machine Learning
Non-monotonic reasoning
Possibilistic logic
Inductive logic programming
Possibilistic logic
Non-monotonic reasoning
Exception handling
Priority
Algorithmics
Competitive algorithms
Circumscription
Possibility theory
First order logic
Simulated annealing
Artificial intelligence
Mathematical programming
ISSN:0004-3702, 1872-7921
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this paper we propose a new formalization of the inductive logic programming (ILP) problem for a better handling of exceptions. It is now encoded in first-order possibilistic logic. This allows us to handle exceptions by means of prioritized rules, thus taking lessons from non-monotonic reasoning. Indeed, in classical first-order logic, the exceptions of the rules that constitute a hypothesis accumulate and classifying an example in two different classes, even if one is the right one, is not correct. The possibilistic formalization provides a sound encoding of non-monotonic reasoning that copes with rules with exceptions and prevents an example to be classified in more than one class. The benefits of our approach with respect to the use of first-order decision lists are pointed out. The possibilistic logic view of ILP problem leads to an optimization problem at the algorithmic level. An algorithm based on simulated annealing that in one turn computes the set of rules together with their priority levels is proposed. The reported experiments show that the algorithm is competitive to standard ILP approaches on benchmark examples.
ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2007.04.016