Eeciently Implementing Slg Resolution

SLG is a table-oriented resolution method that is gaining increasing attention due to its ability to combine the deductive database, non-monotonic reasoning and logic programming paradigms. SLG resembles SLD in that it admits a tuple-at-a-time resolution method, so it can make use of many of the techniques developed in implementing SLD over the past decade. Indeed, a program can contain any mixture of SLG and SLD evaluated predicates. As a result SLG forms a natural basis for extending Prolog systems. SLG can be eeciently implemented using a WAM-style abstract machine, the SLG-WAM. The SLG-WAM has been implemented for stratiied programs, and design is underway for extensions to general programs. Performance results for stratiied programs are available. They indicate rstly when executing SLD, the overhead of the SLG-WAM compared to a similar WAM implementation is minimal, and usually less than 10%. Further results indicate that SLG derivation may be expected to be surprisingly competitive with SLD derivation on numerous datalog programs, even when SLD terminates and contains no redundant subcomputations. Finally, performance comparisons indicate that the SLG-WAM is about an order of magnitude faster than current deductive database systems even for datalog queries. The results, taken as a whole call into question traditional arguments about top-down versus bottom-up evaluation eeciency, and also indicate that implementation of SLG evaluation is a eld of research worthy of general study.