Tabling for non-monotonic programming

Terrance Swift

Research output: Contribution to journalArticlepeer-review


Non-monotonic extensions add power to logic programs. However, the main logic programming language, Prolog, is widely recognized as inadequate to implement these extensions due to its weak termination and complexity properties. By extending Prolog's SLD resolution with tabling, Prolog can be improved in several ways. Tabling can allow a logic programming system to compute the well-founded semantics for programs with bounded term depth, and to do so with polynomial data complexity. By exploiting these properties, tabling allows a variety of non-monotonic extensions to be efficiently implemented, and used to solve practical problems. In this paper we describe tabling as it is implemented in the XSB system and show how it can be used to construct metainterpreters (or preprocessors) for two sample formalisms: the Well-Founded Semantics with Explicit Negation, and Generalized Annotated Logic Programs. We also describe how non-monotonic extensions are used in practical applications such as psychiatric diagnosis, extraction of information from poorly structured textual data, and model checking.

Original languageEnglish (US)
Pages (from-to)201-240
Number of pages40
JournalAnnals of Mathematics and Artificial Intelligence
Issue number3-4
StatePublished - 1999

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics


Dive into the research topics of 'Tabling for non-monotonic programming'. Together they form a unique fingerprint.

Cite this