We discuss the adoption of a three-valued setting for inductive concept learning. Distinguishing between what is true, what is false and what is unknown can be useful in situations where decisions have to be taken on the basis of scarce information. In a three-valued setting, we learn a definition for both the target concept and its opposite, considering positive and negative examples as instances of two disjoint classes. To this purpose, we adopt extended logic programs under a well-founded semantics as the representation formalism for learning. Explicit negation is used in order to represent the opposite concept, while default negation is used to ensure consistency and to handle exceptions to general rules. Exceptions to a concept are identified from examples of the opposite training set. Exceptions can be generalized, in their turn, by learning within a hierarchy of defaults. Standard Inductive Logic Programming techniques are used to learn the concept and its opposite. Depending on the adopted technique, we can learn the most general or the least general definition. Thus, four epistemological varieties occur, resulting from the combination of most general and least general solutions for the positive and negative concept. We discuss the factors that should be taken into account when choosing and strategically combining the generality levels.

Strategies for Learning with Extended Logic Programs

LAMMA, Evelina;RIGUZZI, Fabrizio;
1998

Abstract

We discuss the adoption of a three-valued setting for inductive concept learning. Distinguishing between what is true, what is false and what is unknown can be useful in situations where decisions have to be taken on the basis of scarce information. In a three-valued setting, we learn a definition for both the target concept and its opposite, considering positive and negative examples as instances of two disjoint classes. To this purpose, we adopt extended logic programs under a well-founded semantics as the representation formalism for learning. Explicit negation is used in order to represent the opposite concept, while default negation is used to ensure consistency and to handle exceptions to general rules. Exceptions to a concept are identified from examples of the opposite training set. Exceptions can be generalized, in their turn, by learning within a hierarchy of defaults. Standard Inductive Logic Programming techniques are used to learn the concept and its opposite. Depending on the adopted technique, we can learn the most general or the least general definition. Thus, four epistemological varieties occur, resulting from the combination of most general and least general solutions for the positive and negative concept. We discuss the factors that should be taken into account when choosing and strategically combining the generality levels.
1998
Inductive Logic Programming; Extended Logic Programs
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1194204
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact