Linear Tabulated Resolution Based on Prolog Control Strategy

Theory and Practice of Logic Programming (Impact Factor: 0.29). 04/2000; DOI: 10.1017/S1471068400001010
Source: CiteSeer

ABSTRACT Infinite loops and redundant computations are long recognized open problems in Prolog. Two ways have been explored to resolve these problems: loop checking and tabling. Loop checking can cut infinite loops, but it cannot be both sound and complete even for function-free logic programs. Tabling seems to be an effective way to resolve infinite loops and redundant computations. However, existing tabulated resolutions, such as OLDT-resolution, SLG-resolution, and Tabulated SLS-resolution, are non-linear because they rely on the solution-lookup mode in formulating tabling. The principal disadvantage of non-linear resolutions is that they cannot be implemented using a simple stack-based memory structure like that in Prolog. Moreover, some strictly sequential operators such as cuts may not be handled as easily as in Prolog. In this paper, we propose a hybrid method to resolve infinite loops and redundant computations. We combine the ideas of loop checking and tabling to establish a linear tabul...

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    ABSTRACT: Summary Tabling is a technique that can get rid of innite loops and redundant computations in the execution of recursive logic programs. The main idea of tabling is to memorize the answers to subgoals and use the answers to resolve their variant descendents. Tabling helps narrow the gap between declarative and procedural readings of logic programs. It not only is useful in the problem domains that motivated its birth, such as program analysis, parsing, deductive database, and theorem proving, but also has been found essential in several other problem domains such as model checking, learning, and data mining. Early resolution mechanisms proposed for tabling such as OLDT rely on suspension and resumption of subgoals to compute xp oints. Recently, a new resolution framework called linear tabling, envisioned by the proposer and several other researchers, has received considerable attention because of its simplicity, ease of implementation, and good space eciency . The idea of linear tabling is to use depth-rst iterative deepening rather than suspension to compute xp oints. Linear tabling is still immature compared with OLDT and a great of potential remains to be exploited. The objective of this project is to qualitatively and quantitatively analyze possible strategies and propose eectiv e optimization techniques to make it sustainable to large applications such as natural language and data mining

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May 31, 2014