Results 1  10
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14
ArcConsistency and ArcConsistency Again
 Artificial Intelligence
, 1994
"... Constraint networks are known as a useful way to formulate problems such as design, scene labeling, temporal reasoning, and more recently natural language parsing. The problem of the existence of solutions in a constraint network is NPcomplete. Hence, consistency techniques have been widely studied ..."
Abstract

Cited by 135 (11 self)
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Constraint networks are known as a useful way to formulate problems such as design, scene labeling, temporal reasoning, and more recently natural language parsing. The problem of the existence of solutions in a constraint network is NPcomplete. Hence, consistency techniques have been widely studied to simplify constraint networks before or during the search of solutions. Arcconsistency is the most used of them. Mohr and Henderson [Moh&Hen86] have proposed AC4, an algorithm having an optimal worstcase time complexity. But it has two drawbacks: its space complexity and its average time complexity. In problems with many solutions, where the size of the constraints is large, these drawbacks become so important that users often replace AC4 by AC3 [Mac&Fre85], a nonoptimal algorithm. In this paper, we propose a new algorithm, AC6, which keeps the optimal worstcase time complexity of AC4 while working out the drawback of space complexity. More, the average time complexity of AC6 is optimal for constraint networks where nothing is known about the semantic of the constraints. At the end of the paper, experimental results show how much AC6 outperforms AC3 and AC4. 1.
Directional Resolution: The DavisPutnam Procedure, Revisited
 IN PROCEEDINGS OF KR94
, 1994
"... The paper presents an algorithm called directional resolution, a variation on the original DavisPutnam algorithm, and analyzes its worstcase behavior as a function of the topological structure of propositional theories. The concepts of induced width and diversity are shown to play a key role in ..."
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Cited by 100 (21 self)
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The paper presents an algorithm called directional resolution, a variation on the original DavisPutnam algorithm, and analyzes its worstcase behavior as a function of the topological structure of propositional theories. The concepts of induced width and diversity are shown to play a key role in bounding the complexity of the procedure. The importance of our analysis lies in highlighting structurebased tractable classes of satisfiability and in providing theoretical guarantees on the time and space complexity of the algorithm. Contrary to previous assessments, we show that for many theories directional resolution could be an effective procedure. Our empirical tests confirm theoretical prediction, showing that on problems with a special structure, namely ktree embeddings (e.g. chains, (k,m)trees), directional resolution greatly outperforms one of the most effective satisfiability algorithms known to date, the popular DavisPutnam procedure. Furthermore, combining a bounded...
Distributed constrained heuristic search
 IEEE Transactions on Systems, Man, and Cybernetics
, 1991
"... In this paper we present a model of decentralized problem solving, called Distributed Constrained Heuristic Search (DCHS) that provides both structure and focus in individual agent search spaces so as to optimize decisions in the global space. The model achieves this by integrating decentralized con ..."
Abstract

Cited by 90 (11 self)
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In this paper we present a model of decentralized problem solving, called Distributed Constrained Heuristic Search (DCHS) that provides both structure and focus in individual agent search spaces so as to optimize decisions in the global space. The model achieves this by integrating decentralized constraint satisfaction and heuristic search. It is a formalism suitable for describing a large set of DAI problems. We introduce the notion of textures that allow agents to operate in an asynchronous concurrent manner. The employment of textures coupled with distributed asynchronous backjumping (DAB), a type of distributed dependencydirected backtracking that we have developed, enables agents to instantiate variables in such a way as to substantially reduce backtracking. We have experimentally tested our approach in the domain of decentralized jobshop scheduling. A formulation of distributed jobshop scheduling as a DCHS is presented as well as experimental results.
A complexity analysis of spacebounded learning algorithms for the constraint satisfaction problem
 In Proceedings of the Thirteenth National Conference on Artificial Intelligence
, 1996
"... Learning during backtrack search is a spaceintensive process that records information (such as additional constraints) in order to avoid redundant work. In this paper, we analyze the effects of polynomialspacebounded learning on runtime complexity of backtrack search. One spacebounded learning sc ..."
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Cited by 77 (2 self)
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Learning during backtrack search is a spaceintensive process that records information (such as additional constraints) in order to avoid redundant work. In this paper, we analyze the effects of polynomialspacebounded learning on runtime complexity of backtrack search. One spacebounded learning scheme records only those constraints with limited size, and another records arbitrarily large constraints but deletes those that become irrelevant to the portion of the search space being explored. We find that relevancebounded learning allows better runtime bounds than sizebounded learning on structurally restricted constraint satisfaction problems. Even when restricted to linear space, our relevancebounded learning algorithm has runtime complexity near that of unrestricted (exponential spaceconsuming) learning schemes.
DeadEnd Driven Learning
, 1994
"... The paper evaluates the effectiveness of learning for speeding up the solution of constraint satisfaction problems. It extends previous work (Dechter 1990) by introducing a new and powerful variant of learning and by presenting an extensive empirical study on much larger and more difficult problem i ..."
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Cited by 75 (5 self)
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The paper evaluates the effectiveness of learning for speeding up the solution of constraint satisfaction problems. It extends previous work (Dechter 1990) by introducing a new and powerful variant of learning and by presenting an extensive empirical study on much larger and more difficult problem instances. Our results show that learning can speed up backjumping when using either a fixed or dynamic variable ordering. However, the improvement with a dynamic variable ordering is not as great, and for some classes of problems learning is helpful only when a limit is placed on the size of new constraints learned.
Intelligent Backtracking Techniques for Job Shop Scheduling
 In Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning
, 1992
"... This paper studies a version of the job shop scheduling problem in which some operations have to be scheduled within nonrelaxable time windows (i.e. earliest/latest possible start time windows). This problem is a wellknown NPcomplete Constraint Satisfaction Problem (CSP). A popular method for solv ..."
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Cited by 33 (4 self)
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This paper studies a version of the job shop scheduling problem in which some operations have to be scheduled within nonrelaxable time windows (i.e. earliest/latest possible start time windows). This problem is a wellknown NPcomplete Constraint Satisfaction Problem (CSP). A popular method for solving these types of problems consists in using depthfirst backtrack search. Our earlier work focused on developing efficient consistency enforcing techniques and efficient variable /value ordering heuristics to improve the efficiency of this procedure. In this paper, we combine these techniques with new lookback schemes that help the search procedure recover from socalled deadend search states (i.e. partial solutions that cannot be completed without violating some constraints). More specifically, we successively describe three intelligent backtracking schemes: Dynamic Consistency Enforcement dynamically enforces higher levels of consistency in selected critical subproblems, Learning From Fa...
In search of the best constraint satisfaction search: An empirical evaluation
 In AAAI94: Proceedings of the Twelfth National Conference on Artificial Intelligence
, 1994
"... We present the results of an empirical study of several constraint satisfaction search algorithms and heuristics. Using a random problem generator that allows us to create instances with given characteristics, we show how the relative performance of various search methods varies with the number of v ..."
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Cited by 29 (7 self)
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We present the results of an empirical study of several constraint satisfaction search algorithms and heuristics. Using a random problem generator that allows us to create instances with given characteristics, we show how the relative performance of various search methods varies with the number of variables, the tightness of the constraints, and the sparseness of the constraint graph. Aversion of backjumping using a dynamic variable ordering heuristic is shown to be extremely e ective on a wide range of problems. We conducted our experiments with problem instances drawn from the 50 % satis able range. 1.
A Generic Framework for ConstraintDirected Search and Scheduling
, 1998
"... This article introduces a generic framework for constraintdirected search. The research literature in constraintdirected scheduling is placed within the framework both to provide insight into, and examples of, the framework and to allow a new perspective on the scheduling literature. We show how ..."
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Cited by 22 (1 self)
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This article introduces a generic framework for constraintdirected search. The research literature in constraintdirected scheduling is placed within the framework both to provide insight into, and examples of, the framework and to allow a new perspective on the scheduling literature. We show how a number of algorithms from the constraintdirected–scheduling research can be conceptualized within the framework. This conceptualization allows us to identify and compare variations of components of our framework and provides new perspective on open research issues. We discuss the prospects for an overall comparison of scheduling strategies and show that firm conclusions visavis such a comparison are not supported by the literature. Pur principal
Models and Techniques of Dynamic DemandResponsive Transportation Planning
 HTTP://WWW.CGI.COM/WEB2/GOVT/MODELS.HTML SAMPO (SYSTEM FOR ADVANCED MANAGEMENT OF PUBLIC TRANSPORT OPERATIONS), 1995–1997, HTTP://WWW.OKANECOM.FI/SAMPO/ ET SAMPLUS (EXTENSION DE SAMPO), 1999 SESAME CONSORTIUM, 1999, SESAME FINAL REPORT, CERTU
, 1996
"... This article provides an overview of stateoftheart technologies relevant to dynamic transportation planning problems that involve the reactive routing nnd scheduling of a fleet of vehicles in response to dynamically changing transportation demands. Specifically, we focus on a new class of compl ..."
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Cited by 5 (1 self)
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This article provides an overview of stateoftheart technologies relevant to dynamic transportation planning problems that involve the reactive routing nnd scheduling of a fleet of vehicles in response to dynamically changing transportation demands. Specifically, we focus on a new class of complex transportation planning problems, which we refer to as the "Dynamic DialARide Problem with Multiple Acceptable Destinations and/or Origins" (DDARPMADO). While this class of dynamic problems is representative of a number of practical transportation problems, it does not appear to have been the object of prior studies. This is not to say that techniques proposed for simpler routing and scheduling problems cannot be brought to bear on this problem. To the contrary, our survey shows that a number of techniques developed
Representation and Reasoning with NonBinary Constraints
, 2001
"... Many problems from the \real world" can be eciently expressed as constraint satisfaction problems (CSPs). Most of these can be naturally modelled using nary (or nonbinary) constraints. Representing problems with nary constraints and reasoning with them is therefore very important in constraint sa ..."
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Cited by 3 (0 self)
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Many problems from the \real world" can be eciently expressed as constraint satisfaction problems (CSPs). Most of these can be naturally modelled using nary (or nonbinary) constraints. Representing problems with nary constraints and reasoning with them is therefore very important in constraint satisfaction. However, issues regarding nary constraints have been neglected compared to binary constraints. The reasons were the simplicity of dealing with binary constraints compared to nary and the fact that any nonbinary CSP can be encoded into an equivalent binary. This thesis makes an empirical and theoretical study on representation and solution methods for nary CSPs. The results we present demonstrate the importance of the choice of representation and reasoning techniques in nary problems.