Similarity-based SLD resolution and its implementation in an extended Prolog system

AbstractECLiPSe is a Prolog-based programming system, aimed at the development and deployment of constraint programming applications. It is also used for teaching most aspects of combinatorial problem solving, for example, problem modelling, constraint programming, mathematical programming and search techniques. It uses an extended Prolog as its high-level modelling and control language, complemented by several constraint solver libraries, interfaces to third-party solvers, an integrated development environment and interfaces for embedding into host environments. This paper discusses language extensions, implementation aspects, components, and tools that we consider relevant on the way from Logic Programming to Constraint Logic Programming.

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A prolog technology theorem prover: Implementation by an extended prolog compiler

8th International Conference on Automated Deduction - Lecture Notes in Computer Science ◽

10.1007/3-540-16780-3_122 ◽

1986 ◽

pp. 573-587 ◽

Cited By ~ 19

Author(s):

Mark E. Stickel

Keyword(s):

Theorem Prover ◽

Extended Prolog

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DURAL: an extended Prolog language

RIMS Symposia on Software Science and Engineering - Lecture Notes in Computer Science ◽

10.1007/3-540-11980-9_16 ◽

1983 ◽

pp. 73-87 ◽

Cited By ~ 2

Author(s):

Shigeki Goto

Keyword(s):

Extended Prolog

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SLD-resolution

Foundations of Inductive Logic Programming - Lecture Notes in Computer Science ◽

10.1007/3-540-62927-0_7 ◽

1997 ◽

pp. 105-126

Keyword(s):

Sld Resolution

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Logical reduction of metarules

Machine Learning ◽

10.1007/s10994-019-05834-x ◽

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.

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