Eric Monfroy

Chaotic iteration for distributed constraint propagation

We propose a generic l&mework for distributed constraint propagation based on the notion of chaotic iteration. Our algorithm applies to distributed constraint satisfaction problems, and also leads to signiiicant speed-ups on distributions of constraint satisfaction problems.

Implementing non-linear constraints with cooperative solvers

We investigate the use of cooperation between solvers in the scheme of constraint logic programming languages over the domain of non-linear polynomial constraints. Instead of using a general and often inefficient decision procedure we propose a new approach for handling these constraints by cooperating specialised solvers. Our approach requires the design of a client/server architecture to enable communication between the various components. The main modules are a linear solver, a non-linear solver, a constraint manager, a communication protocol component and an answer processor module. This work is motivated by the need for a declarative system for robot motion planning and geometric problem solving. We have implemented a prototype called groak %({bf sf Craisebox{.2ex}o}nstraint {bf sf S}ystem {bf sf %A}r{bf sf raisebox{.2ex}c}hitecture) (textbf{textsf C}raisebox{.2ex}{textbf{textsfo}}nstraint textbf{textsfS}ystem textbf{textsfA}rraisebox{.2ex}{textbf{textsfc}}hit- ecture) to validate our approach using cooperating solvers for non-linear constraints over the real numbers. Our language is illustrated by an example that also shows the advantages of cooperation.

New Trends in Constraints

Invited Contributions and Surveys.- Interval Constraints: Results and Perspectives.- A Constraint-Based Language for Virtual Agents.- Constraint (Logic) Programming: A Survey on Research and Applications.- OPL Script: Composing and Controlling Models.- Constraint Propagation and Manipulation.- Some Remarks on Boolean Constraint Propagation.- Abstracting Soft Constraints.- Decomposable Constraints.- Generating Propagation Rules for Finite Domains: A Mixed Approach.- Ways of Maintaining Arc Consistency in Search Using the Cartesian Representation.- Constraint Programming.- Combining Constraint Logic Programming Techniques for Solving Linear Problems.- Quantitative Observables and Averages in Probabilistic Constraint Programming.- Dynamic Constraint Models for Planning and Scheduling Problems.- A Finite Domain CLP Solver on Top of Mercury.- Rule-Based Constraint Programming.- Rule Based Programming with Constraints and Strategies.- Proving Termination of Constraint Solver Programs.- Projection in Adaptive Constraint Handling.ing Soft Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 S. Bistarelli (University of Pisa), Philippe Codognet (University of Paris 6), Y. Georget (INRIA Roquencourt), and Francesca Rossi (University of Padova) Decomposable Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 Ian Gent (University of St. Andrews), Kostas Stergiou (University of Strathclyde), and Toby Walsh (University of York) Generating Propagation Rules for Finite Domains: A Mixed Approach . . . . 150 Christophe Ringeissen (LORIA-INRIA) and Eric Monfroy (CWI) Ways of Maintaining Arc Consistency in Search Using the Cartesian Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Marius-C. Silaghi, Djamila Sam-Haroud, and Boi Faltings (Swiss Institute of Technology) Constraint Programming Combining Constraint Logic Programming Techniques for Solving Linear Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 Reem Bahgat and Samir E. Abdel-Rahman (Cairo University)

A coordination-based chaotic iteration algorithm for constraint propagation

In this paper we propose a generic framework for constraint propagation using coordination languages. More precisely, we are concerned with the realization of a coordinationbased version of the Generic Iteration Algorithm for Compound Domains of [2]. Our main motivation is to explain constraint propagation as coordination of cooperative agents, and to provide a flexible, scalable, and generic framework for constraint propagation that overcomes the problems that axe inherent in the parallel and distributed algorithms of [15; 16]. Another benefit of our coordination-based framework is that it does not require special modeling of CSPs.

Coordination of heterogeneous distributed cooperative constraint solving

In this paper we argue for an alternative way of designing cooperative constraint solver systems using a control-oriented coordination language. The idea is to take advantage of the coordination features of MANIFOLD for improving the constraint solver collaboration language of BALI. We demonstrate the validity of our ideas by presenting the advantages of such a realization and its (practical as well as conceptual) improvements of constraint solving. We are convinced that cooperative constraint solving is intrinsically linked to coordination, and that coordination languages, and MANIFOLD in particular, open new horizons for systems like BALI.

Using “weaker” functions for constraint propagation over real numbers

In this paper we argue for an alternative way of designing solvers based on interval arithmetic. We achieve constraint propagation over real numbers using chaotic iteration. This is carried out in two steps: first involving computationally “cheap” functions for reducing constraint satisfaction problems followed by computationally “expensive” functions for enforcing a local consistency property. This technique improves the global performances of the propagation mechanism without adding any new mathematical machinery.

An Environment for Designing/Executing Constraint Solver Collaborations

Abstract Abstract Constraint programming is a paradigm based on the notion of constraints and mechanisms for their resolution. Thus, the key point of this class of languages is not only to offer a wide class of constraints for declarativity reasons, but also to treat them efficiently. For this purpose, the need for collaboration i.e., combination and cooperation of solvers is widely recognized. This new concept enables to solve problems that cannot be tackled or efficiently solved with a single solver. Furthermore, the demand for integrating symbolic mathematical tools into automated deduction system has significantly increased. In order to meet these motivations we propose BALI, an environment for designing/executing solver collaborations. BALI is a heterogeneous distributed collaborative problem solving system. It consists of a solver collaboration language and a host language. By providing several construction primitives (as concurrency, parallelism and sequentiality) and several combinators for their composition (as iterator or guarded control), the solver collaboration language enables to build complex solvers from elementary heterogeneous ones. The solvers are encapsulated in order to federate their different knowledge representations. We thus obtain agents that communicate and collaborate with each other. The host language, which is a constraint programming language, furnishes several strategies for manipulating constraints and executing solver collaborations i.e., agent collaborations.

Using coordination for cooperative constraint solving

textabstractIn this paper we argue for an alternative way of designing cooperative constraint solver systems using a control-oriented coordination language. The idea is to take advantage of the coordination features of MANIFOLD for improving the constraint solver collaboration language of BALI. We demonstrate the validity of our ideas by presenting the advantages of such a realization and its (practical as well as conceptual) improvements of constraint solving. We are convinced that cooperative constraint solving is intrinsically linked to coordination, and that coordination languages, and MANIFOLD in particular, open new horizons for systems like BALI.