In this paper we present new techniques for improving backtracking based Quantified Constraint Satisfaction Problem (QCSP) solvers. QCSP is a generalization of CSP in which variab...
Abstract. Empirical hardness models are a recent approach for studying NP-hard problems. They predict the runtime of an instance using efficiently computable features. Previous res...
We propose a new top down search-based algorithm for compiling AND/OR Multi-Valued Decision Diagrams (AOMDDs), as representations of the optimal set of solutions for constraint opt...
Robert Mateescu, Radu Marinescu 0002, Rina Dechter
Abstract. A common type of symmetry is when both variables and values partition into interchangeable sets. Polynomial methods have been introduced to eliminate all symmetric soluti...
Yat Chiu Law, Jimmy Ho-Man Lee, Toby Walsh, J. Y. ...
Probabilistic Choice Operators (PCOs) are convenient tools to model uncertainty in CP. They are useful to implement randomized algorithms and stochastic processes in the concurrent...
Constraints have played a central role in cp because they capture key substructures of a problem and efficiently exploit them to boost inference. This paper intends to do the same ...
Abstract. The SEQUENCE constraint is useful in modelling car sequencing, rostering, scheduling and related problems. We introduce half a dozen new encodings of the SEQUENCE constra...
Sebastian Brand, Nina Narodytska, Claude-Guy Quimp...
: This report introduces a geometrical constraint kernel for handling the location in space and time of polymorphic k-dimensional objects subject to various geometrical and time co...
Nicolas Beldiceanu, Mats Carlsson, Emmanuel Poder,...