Efficient Reductants Calculi using Partial Evaluation Techniques with Thresholding

Julián, Pascual; Moreno, Ginés; Penabad, Jaime

doi:10.1016/j.entcs.2007.05.039

Cited by 5 publications

(10 citation statements)

References 9 publications

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“…In this sense, we also plan to take advantage of the experience acquired in [42], where we introduced thresholding techniques for dynamically reducing the size of unfolding trees. Our thresholding techniques have some correspondences with the tabulation method used in [32] in the sense that we also store/update/compare the current best truth degree associated to a given (first order) atom when building its associated (partial) unfolding tree.…”

Section: Final Discussionmentioning

confidence: 99%

“…• The techniques presented so far here as well as those described in [42] can be extended to cope with the possibility of tabulating several thresholds instead of a single one 11 .…”

Section: Final Discussionmentioning

confidence: 99%

“…In particular, observe that we can only simulate complete fuzzy derivations (by performing the corresponding Prolog derivations based on SLD-resolution) but we can not generate partial derivations or even apply a single admissible/interpretive step on a given fuzzy expression. This kind of low-level manipulations are mandatory when trying to incorporate to the tool mechanisms for generating derivations of a fixed number of steps, rearranging the body of a program rule, applying substitutions to its head, etc... in order to implement some program transformation techniques such as the ones presented in this paper as well as those described in [20,18,42].…”

Section: Final Discussionmentioning

confidence: 99%

“…Therefore, to guarantee the termination of the partial evaluation process is important to investigate some appropriate technique able to keep this set finite. (3) To improve the method for the construction of the so called P E-reductants by means of a refined algorithm based on unfolding with a set of dynamic thresholds (we have some preliminary results in this line [42]) and tabulation techniques (in the style of [39,40,32]), in order to prune tree branches and properly decide which nodes in the unfolding tree must be selected to be exploited. (4) To allow the presence of variables in the heads of P E-reductants.…”

Section: Conclusion and Further Researchmentioning

confidence: 99%

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An improved reductant calculus using fuzzy partial evaluation techniques

Julián¹,

Moreno

Penabad

2009

Fuzzy Sets and Systems

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Partial evaluation (PE) is an automatic program transformation technique aiming to obtain, among other advantages, the optimization of a program with respect to parts of its input: hence, it is also known as program specialization. This paper introduces the subject of PE into the field of fuzzy logic programming. We define the concept of PE for multi-adjoint logic programs and goals, and apart from discussing the benefits achieved by this technique, we also introduce in the fuzzy setting a completely novel application of PE which allows us the computation of reductants guaranteeing completeness properties without harming the computational efficiency. Reductants are a special kind of fuzzy rules which constitute an essential theoretical tool for proving correctness properties. As observed in the specialized literature, a multi-adjoint logic program, when interpreted on a partially ordered lattice, has to include all its reductants in order to preserve the (approximate) completeness property. This introduces severe penalties in the implementation of efficient multiadjoint logic programming systems: not only the size of programs increases but also their execution time. In this paper we define a refinement to the notion of reductant based on PE techniques, that we call P E-reductant. We establish the main properties of P E-reductants (i.e., the classical concept of reductant and the new notion of P E-reductant are both, semantically and operationally, equivalent) and, what is the best, we demonstrate that our refined notion of P E-reductant is even able to increase the efficiency of multi-adjoint logic programs.

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Section: Final Discussionmentioning

confidence: 99%

“…• The techniques presented so far here as well as those described in [42] can be extended to cope with the possibility of tabulating several thresholds instead of a single one 11 .…”

Section: Final Discussionmentioning

confidence: 99%

Section: Final Discussionmentioning

confidence: 99%

Section: Conclusion and Further Researchmentioning

confidence: 99%

See 2 more Smart Citations

An improved reductant calculus using fuzzy partial evaluation techniques

Julián¹,

Moreno

Penabad

2009

Fuzzy Sets and Systems

Self Cite

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“…In [13] a fuzzy partial evaluation framework was introduced for specializing multi-adjoint logic programs. Moreover, it was pointed out that if the proposed partial evaluation process is combined with thresholding techniques, the following benefits can be obtained:…”

Section: Introductionmentioning

confidence: 99%

Thresholded Tabulation in a Fuzzy Logic Setting

Julián

Medina

Moreno

et al. 2009

Electronic Notes in Theoretical Computer Science

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Fuzzy logic programming represents a flexible and powerful declarative paradigm amalgamating fuzzy logic and logic programming, for which there exists different promising approaches described in the literature. In this paper we propose an improved fuzzy query answering procedure for the so called multi-adjoint logic programming approach, which avoids the re-evaluation of goals and the generation of useless computations thanks to the combined use of tabulation with thresholding techniques. The general idea is that, when trying to perform a computation step by using a given program rule R, we firstly analyze if such step might contribute to reach further significant solutions (non tabulated yet). When it is the case, it is possible to avoid a useless computation step via a rule R by using thresholds and filters based on the truth degree of R, as well as a safe, accurate and dynamic estimation of the maximum truth degree associated to its body.

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Programming with Fuzzy Logic Rules by Using the FLOPER Tool

Morcillo

Moreno

2008

Rule Representation, Interchange and Reasoning on the Web

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Efficient Reductants Calculi using Partial Evaluation Techniques with Thresholding

Cited by 5 publications

References 9 publications

An improved reductant calculus using fuzzy partial evaluation techniques

An improved reductant calculus using fuzzy partial evaluation techniques

Thresholded Tabulation in a Fuzzy Logic Setting

Programming with Fuzzy Logic Rules by Using the FLOPER Tool

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