71,694 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:
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
(Co-)Inductive semantics for Constraint Handling Rules
In this paper, we address the problem of defining a fixpoint semantics for
Constraint Handling Rules (CHR) that captures the behavior of both
simplification and propagation rules in a sound and complete way with respect
to their declarative semantics. Firstly, we show that the logical reading of
states with respect to a set of simplification rules can be characterized by a
least fixpoint over the transition system generated by the abstract operational
semantics of CHR. Similarly, we demonstrate that the logical reading of states
with respect to a set of propagation rules can be characterized by a greatest
fixpoint. Then, in order to take advantage of both types of rules without
losing fixpoint characterization, we present an operational semantics with
persistent. We finally establish that this semantics can be characterized by
two nested fixpoints, and we show the resulting language is an elegant
framework to program using coinductive reasoning.Comment: 17 page
On empirical methodology, constraints, and hierarchy in artificial grammar learning
This paper considers the AGL literature from a psycholinguistic perspective. It first presents a taxonomy of the experimental familiarization test procedures used, which is followed by a consideration of shortcomings and potential improvements of the empirical methodology. It then turns to reconsidering the issue of grammar learning from the point of view of acquiring constraints, instead of the traditional AGL approach in terms of acquiring sets of rewrite rules. This is, in particular, a natural way of handling long‐distance dependences. The final section addresses an underdeveloped issue in the AGL literature, namely how to detect latent hierarchical structure in AGL response patterns
Logic Programming Applications: What Are the Abstractions and Implementations?
This article presents an overview of applications of logic programming,
classifying them based on the abstractions and implementations of logic
languages that support the applications. The three key abstractions are join,
recursion, and constraint. Their essential implementations are for-loops, fixed
points, and backtracking, respectively. The corresponding kinds of applications
are database queries, inductive analysis, and combinatorial search,
respectively. We also discuss language extensions and programming paradigms,
summarize example application problems by application areas, and touch on
example systems that support variants of the abstractions with different
implementations
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
A survey of planning and scheduling research at the NASA Ames Research Center
NASA Ames Research Center has a diverse program in planning and scheduling. This paper highlights some of our research projects as well as some of our applications. Topics addressed include machine learning techniques, action representations and constraint-based scheduling systems. The applications discussed are planetary rovers, Hubble Space Telescope scheduling, and Pioneer Venus orbit scheduling
- …