Magic Sets vs. SLD-Resolution

1996 ◽  
pp. 185-203 ◽  
Author(s):  
Stefan Brass
Keyword(s):  
Magic Sets ◽  
Sld Resolution

scholarly journals Magic sets for polynomials of degree n

2021 ◽  
Vol 609 ◽  
pp. 413-441
Author(s):  
Lorenz Halbeisen ◽  
Norbert Hungerbühler ◽  
Salome Schumacher
Keyword(s):  
Magic Sets

Dynamic Magic Sets and super-coherent answer set programs

2011 ◽  
Vol 24 (2) ◽  
pp. 125-145 ◽  
Author(s):  
Mario Alviano ◽  
Wolfgang Faber
Keyword(s):  
Magic Sets ◽  
Answer Set

scholarly journals Logical reduction of metarules

2019 ◽  
Vol 109 (7) ◽  
pp. 1323-1369
Author(s):  
Andrew Cropper ◽  
Sophie Tourret
Keyword(s):  
Logic Programming ◽  
Open Problem ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Learning Task ◽  
Reduction Technique ◽  
Trade Off ◽  
Hypothesis Space ◽  
General Derivation ◽  
Sld Resolution

AbstractMany forms of inductive logic programming (ILP) use metarules, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Deciding which metarules to use for a given learning task is a major open problem and is a trade-off between efficiency and expressivity: the hypothesis space grows given more metarules, so we wish to use fewer metarules, but if we use too few metarules then we lose expressivity. In this paper, we study whether fragments of metarules can be logically reduced to minimal finite subsets. We consider two traditional forms of logical reduction: subsumption and entailment. We also consider a new reduction technique called derivation reduction, which is based on SLD-resolution. We compute reduced sets of metarules for fragments relevant to ILP and theoretically show whether these reduced sets are reductions for more general infinite fragments. We experimentally compare learning with reduced sets of metarules on three domains: Michalski trains, string transformations, and game rules. In general, derivation reduced sets of metarules outperform subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times.

scholarly journals Narrowing vs. SLD-resolution

1988 ◽  
Vol 59 (1-2) ◽  
pp. 3-23 ◽  
Author(s):  
Pier Giorgio Bosco ◽  
Elio Giovannetti ◽  
Corrado Moiso
Keyword(s):  
Sld Resolution

Non-horn magic sets to incorporate top-down inference into bottom-up theorem proving

1997 ◽  
pp. 176-190 ◽  
Author(s):  
Ryuzo Hasegawa ◽  
Katsumi Inoue ◽  
Yoshihiko Ohta ◽  
Miyuki Koshimura
Keyword(s):  
Theorem Proving ◽  
Top Down ◽  
Bottom Up ◽  
Magic Sets

scholarly journals Heterogeneous SLD resolution

1984 ◽  
Vol 1 (4) ◽  
pp. 297-303 ◽  
Author(s):  
Lee Naish
Keyword(s):  
Sld Resolution

scholarly journals Sound and Complete SLD-Resolution for Bilattice-Based Annotated Logic Programs

2009 ◽  
Vol 225 ◽  
pp. 141-159 ◽  
Author(s):  
Ekaterina Komendantskaya ◽  
Anthony Karel Seda
Keyword(s):  
Logic Programs ◽  
Sld Resolution ◽  
Annotated Logic

M: AN APPROXIMATE REASONING SYSTEM

1993 ◽  
Vol 07 (03) ◽  
pp. 431-440
Author(s):  
QINPING ZHAO ◽  
BO LI
Keyword(s):  
Solution Algorithm ◽  
Approximate Reasoning ◽  
Inference Rules ◽  
Multivalued Logic ◽  
Reasoning System ◽  
Sld Resolution ◽  
Truth Value ◽  
Truth Degrees

A system of multivalued logical equations and its solution algorithm are put forward in this paper. Based on this work we generalize SLD-resolution into multivalued logic and establish the corresponding truth value calculus. As a result, M, an approximate reasoning system, is built. We present the language and inference rules of M. Furthermore, we analyse inconsistency of assignments to truth degrees and give the solving strategies of M.

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