1,312 research outputs found
Intelligent search strategies based on adaptive Constraint Handling Rules
The most advanced implementation of adaptive constraint processing with
Constraint Handling Rules (CHR) allows the application of intelligent search
strategies to solve Constraint Satisfaction Problems (CSP). This presentation
compares an improved version of conflict-directed backjumping and two variants
of dynamic backtracking with respect to chronological backtracking on some of
the AIM instances which are a benchmark set of random 3-SAT problems. A CHR
implementation of a Boolean constraint solver combined with these different
search strategies in Java is thus being compared with a CHR implementation of
the same Boolean constraint solver combined with chronological backtracking in
SICStus Prolog. This comparison shows that the addition of ``intelligence'' to
the search process may reduce the number of search steps dramatically.
Furthermore, the runtime of their Java implementations is in most cases faster
than the implementations of chronological backtracking. More specifically,
conflict-directed backjumping is even faster than the SICStus Prolog
implementation of chronological backtracking, although our Java implementation
of CHR lacks the optimisations made in the SICStus Prolog system. To appear in
Theory and Practice of Logic Programming (TPLP).Comment: Number of pages: 27 Number of figures: 14 Number of Tables:
Constraint Programming viewed as Rule-based Programming
We study here a natural situation when constraint programming can be entirely
reduced to rule-based programming. To this end we explain first how one can
compute on constraint satisfaction problems using rules represented by simple
first-order formulas. Then we consider constraint satisfaction problems that
are based on predefined, explicitly given constraints. To solve them we first
derive rules from these explicitly given constraints and limit the computation
process to a repeated application of these rules, combined with labeling.We
consider here two types of rules. The first type, that we call equality rules,
leads to a new notion of local consistency, called {\em rule consistency} that
turns out to be weaker than arc consistency for constraints of arbitrary arity
(called hyper-arc consistency in \cite{MS98b}). For Boolean constraints rule
consistency coincides with the closure under the well-known propagation rules
for Boolean constraints. The second type of rules, that we call membership
rules, yields a rule-based characterization of arc consistency. To show
feasibility of this rule-based approach to constraint programming we show how
both types of rules can be automatically generated, as {\tt CHR} rules of
\cite{fruhwirth-constraint-95}. This yields an implementation of this approach
to programming by means of constraint logic programming. We illustrate the
usefulness of this approach to constraint programming by discussing various
examples, including Boolean constraints, two typical examples of many valued
logics, constraints dealing with Waltz's language for describing polyhedral
scenes, and Allen's qualitative approach to temporal logic.Comment: 39 pages. To appear in Theory and Practice of Logic Programming
Journa
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
Implementing Type Theory in Higher Order Constraint Logic Programming
International audienceIn this paper we are interested in high-level programming languages to implement the core components of an interactive theorem prover for a dependently typed language: the kernel — responsible for type-checking closed terms — and the elaborator — that manipulates terms with holes or, equivalently, partial proof terms. In the first part of the paper we confirm that λProlog, the language developed by Miller and Nadathur since the 80s, is extremely suitable for implementing the kernel, even when efficient techniques like reduction machines are employed. In the second part of the paper we turn our attention to the elaborator and we observe that the eager generative semantics inherited by Prolog makes it impossible to reason by induction over terms containing metavariables. We also conclude that the minimal extension to λProlog that allows to do so is the possibility to delay inductive predicates over flexible terms, turning them into (set of) constraints to be propagated according to user provided constraint propagation rules. Therefore we propose extensions to λProlog to declare and manipulate higher order constraints, and we implement the proposed extensions in the ELPI system. Our test case is the implementation of an elaborator for a type theory as a CLP extension to a kernel written in plain λProlog
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