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Nogood Recording for Static and Dynamic Constraint Satisfaction Problems
 International Journal of Artificial Intelligence Tools
, 1993
"... Many AI synthesis problems such as planning, scheduling or design may be encoded in a constraint satisfaction problem (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, fo ..."
Abstract

Cited by 111 (5 self)
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Many AI synthesis problems such as planning, scheduling or design may be encoded in a constraint satisfaction problem (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, for many real tasks, the set of constraints to consider may evolve because of the environment or because of user interactions. The problem we consider here is the solution maintenance problem in such a dynamic CSP (DCSP). We propose a new class of constraint recording algorithms called Nogood Recording that may be used for solving both static and dynamic CSPs. It offers an interesting compromise, polynomially bounded in space, between an ATMSlike approach and the usual static constraint satisfaction algorithms. 1 Introduction The constraint satisfaction problem (CSP) model is widely used to represent and solve various AI related problems and provides fundamental tools in areas such as truth...
Incremental Adaptation of Constraint Handling Rule Derivations
 Journal of Applied Arti Intelligence, Special Issue on Constraint Handling Rules
, 1997
"... . Constraint solving in dynamic environments requires an immediate adaptation of previously evaluated solutions of constraint satisfaction problems if these problems are changed. After a change, an adapted solution is preferred which is stable, i.e. as close as possible to the original solution. ..."
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Cited by 6 (2 self)
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. Constraint solving in dynamic environments requires an immediate adaptation of previously evaluated solutions of constraint satisfaction problems if these problems are changed. After a change, an adapted solution is preferred which is stable, i.e. as close as possible to the original solution. A wide range of incremental constraint solving methods for dynamic finite domain constraint satisfaction problems are known which satisfy more or less this additional requirement. Fruhwirth's Constraint Handling Rules (CHRs) are a highlevel language extension to write constraint solvers. Up to now, more than 20 solvers for different kinds of constraints are available, including finitedomain constraints, linear equations and inequations, and set constraints. In this paper, a new general constraint solving method for dynamic constraint satisfaction problems is presented. In detail, a new incremental algorithm is presented which adapts CHR derivations after changes of the initial c...
Two Approaches to the Solution Maintenance Problem in Dynamic Constraint Satisfaction Problems
 In Proceedings of the IJCAI93/SIGMAN Workshop on Knowledgebased Production Planning, Scheduling and Control
, 1993
"... Many AI synthesis problems such as planning or scheduling may be modelized as constraint satisfaction problems (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, for many ..."
Abstract

Cited by 6 (2 self)
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Many AI synthesis problems such as planning or scheduling may be modelized as constraint satisfaction problems (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, for many real tasks, the set of constraints to consider may evolve because of the environment or because of user interactions. The notion of dynamic CSP (DCSP) [DD88] has been proposed to represent such evolutions. The problem we consider here is the solution maintenance problem in a DCSP. Naively applying usual satisfaction algorithms to this problem results in redundant search and inefficiency. Two distinct approaches to this problem are proposed in this paper. The first one, called the Local Changes approach, reuses the previous solution (if any) to compute a new solution. The second one, called the Nogood Recording approach, tries to store in a concise way, an approximate description of the front...
Representation and Practical Handling of Flexibility in Constrained Problems
, 1992
"... This paper is a synthesis of the first exchanges and studies within a French Artificial Intelligence Research Project on the flexibility in constrained problems [BBB ..."
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This paper is a synthesis of the first exchanges and studies within a French Artificial Intelligence Research Project on the flexibility in constrained problems [BBB
Multiplex Dispensation Order Generation for
"... Abstract. This article introduces the multiplex dispensation order generation problem, a reallife combinatorial problem that arises in the context of analyzing large numbers of short to medium length DNA sequences. The problem is modeled as a constraint optimization problem (COP). We present the C ..."
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Abstract. This article introduces the multiplex dispensation order generation problem, a reallife combinatorial problem that arises in the context of analyzing large numbers of short to medium length DNA sequences. The problem is modeled as a constraint optimization problem (COP). We present the COP, its constraint programming formulation, and a custom search procedure. We give some experimental data supporting our design decisions. One of the lessons learnt from this study is that the ease with which the relevant constraints are expressed can be a crucial factor in making design decisions in the COP model. 1