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Tabling for non‐monotonic programming

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Abstract

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 meta‐interpreters (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.

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  1. Department of Computer Science, SUNY at Stony Brook, Stony Brook, NY, 11794‐4400, USA

    Terrance Swift

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  1. Terrance Swift
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Swift, T. Tabling for non‐monotonic programming. Annals of Mathematics and Artificial Intelligence 25, 201–240 (1999). https://doi.org/10.1023/A:1018990308362

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