948 research outputs found
Automatic Generation of CHR Constraint Solvers
In this paper, we present a framework for automatic generation of CHR solvers
given the logical specification of the constraints. This approach takes
advantage of the power of tabled resolution for constraint logic programming,
in order to check the validity of the rules. Compared to previous works where
different methods for automatic generation of constraint solvers have been
proposed, our approach enables the generation of more expressive rules (even
recursive and splitting rules) that can be used directly as CHR solvers.Comment: to be published in Theory and Practice of Logic Programming, 16
pages, 2 figure
Constraint Handling Rules with Binders, Patterns and Generic Quantification
Constraint Handling Rules provide descriptions for constraint solvers.
However, they fall short when those constraints specify some binding structure,
like higher-rank types in a constraint-based type inference algorithm. In this
paper, the term syntax of constraints is replaced by -tree syntax, in
which binding is explicit; and a new generic quantifier is introduced,
which is used to create new fresh constants.Comment: Paper presented at the 33nd International Conference on Logic
Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1, 2017
16 pages, LaTeX, no PDF figure
Optimal Union-Find in Constraint Handling Rules
Constraint Handling Rules (CHR) is a committed-choice rule-based language
that was originally intended for writing constraint solvers. In this paper we
show that it is also possible to write the classic union-find algorithm and
variants in CHR. The programs neither compromise in declarativeness nor
efficiency. We study the time complexity of our programs: they match the
almost-linear complexity of the best known imperative implementations. This
fact is illustrated with experimental results.Comment: 12 pages, 3 figures, to appear in Theory and Practice of Logic
Programming (TPLP
CHR as grammar formalism. A first report
Grammars written as Constraint Handling Rules (CHR) can be executed as
efficient and robust bottom-up parsers that provide a straightforward,
non-backtracking treatment of ambiguity. Abduction with integrity constraints
as well as other dynamic hypothesis generation techniques fit naturally into
such grammars and are exemplified for anaphora resolution, coordination and
text interpretation.Comment: 12 pages. Presented at ERCIM Workshop on Constraints, Prague, Czech
Republic, June 18-20, 200
CHR(PRISM)-based Probabilistic Logic Learning
PRISM is an extension of Prolog with probabilistic predicates and built-in
support for expectation-maximization learning. Constraint Handling Rules (CHR)
is a high-level programming language based on multi-headed multiset rewrite
rules.
In this paper, we introduce a new probabilistic logic formalism, called
CHRiSM, based on a combination of CHR and PRISM. It can be used for high-level
rapid prototyping of complex statistical models by means of "chance rules". The
underlying PRISM system can then be used for several probabilistic inference
tasks, including probability computation and parameter learning. We define the
CHRiSM language in terms of syntax and operational semantics, and illustrate it
with examples. We define the notion of ambiguous programs and define a
distribution semantics for unambiguous programs. Next, we describe an
implementation of CHRiSM, based on CHR(PRISM). We discuss the relation between
CHRiSM and other probabilistic logic programming languages, in particular PCHR.
Finally we identify potential application domains
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