Sentence examples for constraint networks from inspiring English sources

There is no need to show the importance of arc-consistency in constraint networks.

In this paper we present several basic techniques for achieving parallel execution of constraint networks.

Constraint networks in qualitative spatial and temporal reasoning (QSTR) typically feature variables defined on infinite domains.

This paper presents the basic architecture for acquiring constraint networks from examples classified by the user.

Combined logical reasoning about space-time has been actively evolving over the past decades, particularly in the context of Artificial Intelligence, spatio-temporal databases, ontologies and constraint networks.

Constraint networks are hyper-graphs whose nodes and hyper-edges respectively represent variables and relations between them.

Our approach is based on a factor graph representation of the constraint network.

In a minimal binary constraint network, every tuple of a constraint relation can be extended to a solution.

The assembly is first represented as a two-layer constraint network by describing its functions, attributes and then entities.

The approach consists in formalizing a problem thanks to a constraint network and then apply these constraints to sets of trajectories.

It then uses the max-sum algorithm to optimally solve the resulting tree structured constraint network, and provides a bounded approximation specific to the particular problem instance.