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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 ..."
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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...
Probe Backtrack Search for Minimal Perturbation in Dynamic Scheduling
, 1999
"... . This paper describes an algorithm designed to minimally recongure schedules in response to a changing environment. External factors have caused an existing schedule to become invalid, perhaps due to the withdrawal of resources, or because of changes to the set of scheduled activities. The total s ..."
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Cited by 70 (12 self)
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. This paper describes an algorithm designed to minimally recongure schedules in response to a changing environment. External factors have caused an existing schedule to become invalid, perhaps due to the withdrawal of resources, or because of changes to the set of scheduled activities. The total shift in the start and end times of already scheduled activities should be kept to a minimum. This optimization requirement may be captured using a linear optimization function over linear constraints. However, the disjunctive nature of the resource constraints impairs traditional mathematical programming approaches. The unimodular probing algorithm interleaves constraint programming and linear programming. The linear programming solver handles only a controlled subset of the problem constraints, to guarantee that the values returned are discrete. Using probe backtracking, a complete, repairbased method for search, these values are simply integrated into constraint programming. Unimodular p...
Local Search With Constraint Propagation and ConflictBased Heuristics
, 2002
"... Search algorithms for solving CSP (Constraint Satisfaction Problems) usually fall into one of two main families: local search algorithms and systematic algorithms. Both families have their advantages. Designing hybrid approaches seems promising since those advantages may be combined into a single ap ..."
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Cited by 65 (17 self)
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Search algorithms for solving CSP (Constraint Satisfaction Problems) usually fall into one of two main families: local search algorithms and systematic algorithms. Both families have their advantages. Designing hybrid approaches seems promising since those advantages may be combined into a single approach. In this paper, we present a new hybrid technique. It performs a local search over partial assignments instead of complete assignments, and uses filtering techniques and conflictbased techniques to efficiently guide the search. This new technique benefits from both classical approaches: aprioripruning of the search space from filteringbased search and possible repair of early mistakes from local search. We focus on a specific version of this technique: tabu decisionrepair.Experiments done on openshop scheduling problems show that our approach competes well with the best highly specialized algorithms. 2002 Elsevier Science B.V. All rights reserved.
A Hybrid Search Architecture Applied to Hard Random 3SAT and LowAutocorrelation Binary Sequences
 In Proceedings of the International Conference on Principles and Practice of Constraint Programming
, 2000
"... The hybridisation of systematic and stochastic search is an active research area with potential bene ts for realworld combinatorial problems. This paper shows that randomising the backtracking component of a systematic backtracker can improve its scalability to equal that of stochastic local searc ..."
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Cited by 42 (13 self)
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The hybridisation of systematic and stochastic search is an active research area with potential bene ts for realworld combinatorial problems. This paper shows that randomising the backtracking component of a systematic backtracker can improve its scalability to equal that of stochastic local search. The hybrid may be viewed as stochastic local search in a constrained space, cleanly combining local search with constraint programming techniques. The approach is applied to two very dierent problems. Firstly a hybrid of local search and constraint propagation is applied to hard random 3SAT problems, and is the rst constructive search algorithm to solve very large instances. Secondly a hybrid of local search and branchandbound is applied to lowautocorrelation binary sequences (a notoriously dicult communications engineering problem), and is the rst stochastic search algorithm to nd optimal solutions. These results show that the approach is a promising one for both constraint satisfaction and optimisation problems.
Stable Solutions for Dynamic Constraint Satisfaction Problems
, 1998
"... . An important extension of constraint technology involves problems that undergo changes that may invalidate the current solution. Previous work on dynamic problems sought methods for efficiently finding new solutions. We take a more proactive approach, exploring methods for finding solutions mo ..."
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Cited by 31 (3 self)
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. An important extension of constraint technology involves problems that undergo changes that may invalidate the current solution. Previous work on dynamic problems sought methods for efficiently finding new solutions. We take a more proactive approach, exploring methods for finding solutions more likely to remain valid after changes that temporarily alter the set of valid assignments (stable solutions). To this end, we examine strategies for tracking changes in a problem and incorporating this information to guide search to solutions that are more likely to be stable. In this work search is carried out with a minconflicts hill climbing procedure, and information about change is used to bias value selection, either by distorting the objective function or by imposing further criteria on selection. We study methods that track either value losses or constraint additions, and incorporate information about relative frequency of change into search. Our experiments show that the...
Maximizing Flexibility: A Retraction Heuristic for Oversubscribed Scheduling Problems
, 2003
"... In this paper we consider the solution of scheduling problems that are inherently oversubscribed. ..."
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Cited by 30 (6 self)
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In this paper we consider the solution of scheduling problems that are inherently oversubscribed.
Lifelong Planning A*
, 2005
"... Heuristic search methods promise to find shortest paths for pathplanning problems faster than uninformed search methods. Incremental search methods, on the other hand, promise to find shortest paths for series of similar pathplanning problems faster than is possible by solving each pathplanning p ..."
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Cited by 28 (3 self)
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Heuristic search methods promise to find shortest paths for pathplanning problems faster than uninformed search methods. Incremental search methods, on the other hand, promise to find shortest paths for series of similar pathplanning problems faster than is possible by solving each pathplanning problem from scratch. In this article, we develop Lifelong Planning A * (LPA*), an incremental version of A * that combines ideas from the artificial intelligence and the algorithms literature. It repeatedly finds shortest paths from a given start vertex to a given goal vertex while the edge costs of a graph change or vertices are added or deleted. Its first search is the same as that of a version of A * that breaks ties in favor of vertices with smaller gvalues but many of the subsequent searches are potentially faster because it reuses those parts of the previous search tree that are identical to the new one. We present analytical results that demonstrate its similarity to A * and experimental results that demonstrate its potential advantage in two different domains if the pathplanning problems change only slightly and the changes are close to the goal.
Branching Constraint Satisfaction Problems and Markov Decision Problems compared
 In Proc. of 6th Int. Conf. on Principles and Practices of Constraint Programming
, 2001
"... Introduction We consider a class of resource allocation problems in which a sequence of tasks is presented to a solver, and the solver must nd an assignment for each task when it arrives. There are constraints restricting the assignments that sets of tasks may be given. The global set of possible t ..."
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Cited by 28 (6 self)
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Introduction We consider a class of resource allocation problems in which a sequence of tasks is presented to a solver, and the solver must nd an assignment for each task when it arrives. There are constraints restricting the assignments that sets of tasks may be given. The global set of possible tasks and their associated constraints is known in advance, but only a subset of them will arrive in a given instance of the problem. The solver may choose to reject some requests. The goal of the solver is to specify an assignment for each task arrival, such that no constraints are violated, while optimising some objective function. The problem class can be used to model a number of dierent scenarios  for example: assigning bookings for repairs and car services in a workshop with limited resources, allocating desks to a series of visiting researchers, assigning deliveries to individual couriers, or deciding upon start times for jobs in a dynamic jobshop. We assume there is uncert
Constraint Solving in Uncertain and Dynamic Environments: A Survey
 Constraints
, 2005
"... Abstract. This article follows a tutorial, given by the authors on dynamic constraint solving at CP 2003 [87]. It aims at offering an overview of the main approaches and techniques that have been proposed in the domain of constraint satisfaction to deal with uncertain and dynamic environments. Keywo ..."
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Cited by 22 (2 self)
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Abstract. This article follows a tutorial, given by the authors on dynamic constraint solving at CP 2003 [87]. It aims at offering an overview of the main approaches and techniques that have been proposed in the domain of constraint satisfaction to deal with uncertain and dynamic environments. Keywords: constraint satisfaction problem, uncertainty, change, stability, robustness, flexibility
Systematic versus stochastic constraint satisfaction
 Proc., 14th International Joint Conference on AI
, 1995
"... This panel explores issues of systematic and stochastic control in the context of constraint satisfaction. 1 ..."
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Cited by 20 (2 self)
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This panel explores issues of systematic and stochastic control in the context of constraint satisfaction. 1