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Partial Constraint Satisfaction
, 1992
"... . A constraint satisfaction problem involves finding values for variables subject to constraints on which combinations of values are allowed. In some cases it may be impossible or impractical to solve these problems completely. We may seek to partially solve the problem, in particular by satisfying ..."
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Cited by 427 (23 self)
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. A constraint satisfaction problem involves finding values for variables subject to constraints on which combinations of values are allowed. In some cases it may be impossible or impractical to solve these problems completely. We may seek to partially solve the problem, in particular by satisfying a maximal number of constraints. Standard backtracking and local consistency techniques for solving constraint satisfaction problems can be adapted to cope with, and take advantage of, the differences between partial and complete constraint satisfaction. Extensive experimentation on maximal satisfaction problems illuminates the relative and absolute effectiveness of these methods. A general model of partial constraint satisfaction is proposed. 1 Introduction Constraint satisfaction involves finding values for problem variables subject to constraints on acceptable combinations of values. Constraint satisfaction has wide application in artificial intelligence, in areas ranging from temporal r...
Minimizing Conflicts: A Heuristic Repair Method for ConstraintSatisfaction and Scheduling Problems
 J. ARTIFICIAL INTELLIGENCE RESEARCH
, 1993
"... This paper describes a simple heuristic approach to solving largescale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a valueorder ..."
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Cited by 398 (6 self)
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This paper describes a simple heuristic approach to solving largescale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a valueordering heuristic, the minconflicts heuristic, that attempts to minimize the number of constraint violations after each step. The heuristic can be used with a variety of different search strategies. We demonstrate empirically that on the nqueens problem, a technique based on this approach performs orders of magnitude better than traditional backtracking techniques. We also describe a scheduling application where the approach has been used successfully. A theoretical analysis is presented both to explain why this method works well on certain types of problems and to predict when it is likely to be most effective.
Algorithms for Constraint Satisfaction Problems: A Survey
 AI MAGAZINE
, 1992
"... A large variety of problems in Artificial Intelligence and other areas of computer science can be viewed as a special case of the constraint satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, planning genetic ..."
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Cited by 372 (0 self)
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A large variety of problems in Artificial Intelligence and other areas of computer science can be viewed as a special case of the constraint satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, planning genetic experiments, and the satisfiability problem. A number of different approaches have been developed for solving these problems. Some of them use constraint propagation to simplify the original problem. Others use backtracking to directly search for possible solutions. Some are a combination of these two techniques. This paper presents a brief overview of many of these approaches in a tutorial fashion.
Constraint Hierarchies
 LISP AND SYMBOLIC COMPUTATION
, 1992
"... Constraints allow programmers and users to state declaratively a relation that should be maintained, rather than requiring them to write procedures to maintain the relation themselves. They are thus useful in such applications as programming languages, user interface toolkits, and simulation package ..."
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Cited by 144 (14 self)
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Constraints allow programmers and users to state declaratively a relation that should be maintained, rather than requiring them to write procedures to maintain the relation themselves. They are thus useful in such applications as programming languages, user interface toolkits, and simulation packages. In many situations, it is desirable to be able to state both required and preferential constraints. The required constraints must hold. Since the other constraints are merely preferences, the system should try to satisfy them if possible, but no error condition arises if it cannot. A constraint hierarchy consists of a set of constraints, each labeled as either required or preferred at some strength. An arbitrary number of different strengths is allowed. In the discussion of a theory of constraint hierarchies, we present alternate ways of selecting among competing possible solutions, and prove a number of propositions about the relations among these alternatives. We then outline algorit...
Algorithms for the Satisfiability (SAT) Problem: A Survey
 DIMACS Series in Discrete Mathematics and Theoretical Computer Science
, 1996
"... . The satisfiability (SAT) problem is a core problem in mathematical logic and computing theory. In practice, SAT is fundamental in solving many problems in automated reasoning, computeraided design, computeraided manufacturing, machine vision, database, robotics, integrated circuit design, compute ..."
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Cited by 127 (3 self)
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. The satisfiability (SAT) problem is a core problem in mathematical logic and computing theory. In practice, SAT is fundamental in solving many problems in automated reasoning, computeraided design, computeraided manufacturing, machine vision, database, robotics, integrated circuit design, computer architecture design, and computer network design. Traditional methods treat SAT as a discrete, constrained decision problem. In recent years, many optimization methods, parallel algorithms, and practical techniques have been developed for solving SAT. In this survey, we present a general framework (an algorithm space) that integrates existing SAT algorithms into a unified perspective. We describe sequential and parallel SAT algorithms including variable splitting, resolution, local search, global optimization, mathematical programming, and practical SAT algorithms. We give performance evaluation of some existing SAT algorithms. Finally, we provide a set of practical applications of the sat...
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 ..."
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Cited by 91 (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.
Constraint satisfaction using constraint logic programming
 Artificial Intelligence
, 1992
"... Constraint logic programming (CLP) is a new class of declarative programming languages whose primitive operations are based on constraints (e.g. constraint solving and constraint entailment). CLP languages naturally combine constraint propagation with nondeterministic choices. As a consequence, the ..."
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Cited by 75 (3 self)
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Constraint logic programming (CLP) is a new class of declarative programming languages whose primitive operations are based on constraints (e.g. constraint solving and constraint entailment). CLP languages naturally combine constraint propagation with nondeterministic choices. As a consequence, they are particularly appropriate for solving a variety of combinatorial search problems, using the global search paradigm, with short development time and efficiency comparable to procedural tools based on the same approach. In this paper, we describe how the CLP language cc(FD), a successor of CHIP using consistency techniques over finite domains, can be used to solve two practical applications: testpattern generation and car sequencing. For both applications, we present the cc(FD) program, describe how constraint solving is performed, report experimental results, and compare the approach with existing tools.
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...
Hierarchical Constraint Logic Programming
, 1993
"... A constraint describes a relation to be maintained ..."
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Cited by 67 (3 self)
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A constraint describes a relation to be maintained
Manufacturing Experience with the Contract Net
, 1987
"... We are implementing a control system for a discrete manufacturing environment that partitions tasks using a negotiation protocol like the contract net described by Smith and Davis [24,25,26,3]. The application domain differs in interesting ways from those to which contract nets have previously been ..."
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Cited by 66 (7 self)
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We are implementing a control system for a discrete manufacturing environment that partitions tasks using a negotiation protocol like the contract net described by Smith and Davis [24,25,26,3]. The application domain differs in interesting ways from those to which contract nets have previously been applied. This report