Results 1 
7 of
7
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 ..."
Abstract

Cited by 430 (6 self)
 Add to MetaCart
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.
Spike: Intelligent scheduling of hubble space telescope observations
 Intelligent Scheduling
, 1994
"... ..."
(Show Context)
Minimizing con icts: a heuristic repair methodfor constraint satisfaction andscheduling problems
 Artif. Intell
, 1992
"... Abbreviated Title: \Minimizing Con icts: A Heuristic Repair Method" 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 th ..."
Abstract

Cited by 36 (1 self)
 Add to MetaCart
Abbreviated Title: \Minimizing Con icts: A Heuristic Repair Method" 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 avalueordering heuristic, the mincon icts heuristic, that attempts to minimize the number of constraint violations after each step. The heuristic can be used with a variety of di erent 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 One of the most promising general approaches for solving combinatorial search problems is to generate an
Analyzing a Heuristic Strategy for ConstraintSatisfaction and Scheduling
 in Intelligent Scheduling
, 1994
"... 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 ..."
Abstract

Cited by 27 (3 self)
 Add to MetaCart
(Show Context)
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. On the nqueens problem, a technique based on this approach performs orders of magnitude better than traditional backtracking techniques. The technique has also been used for scheduling the Hubble Space telescope. 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. 1 Introduction One of the most promising general approaches for solving combinatorial search problems is to generate an initial...
A Framework for Integrating Artificial Neural Networks and Logic Programming
 INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
, 1995
"... Many reallife problems belong to the class of constraint satisfaction problems (CSP's), which are NPcomplete, and some NPhard, in general. When the problem size grows, it becomes difficult to program solutions and to execute the solution in a timely manner. In this paper, we present a gen ..."
Abstract

Cited by 11 (8 self)
 Add to MetaCart
Many reallife problems belong to the class of constraint satisfaction problems (CSP's), which are NPcomplete, and some NPhard, in general. When the problem size grows, it becomes difficult to program solutions and to execute the solution in a timely manner. In this paper, we present a general framework for integrating artificial neural networks and logic programming to provide an efficient and yet easytoprogram environment for solving CSP's. To realize this framework, we propose a novel constraint logic programming language PROCLANN. Operationally, PROCLANN uses the standard goal reduction strategy as frontend to generate constraints and an efficient backend constraintsolver based on artificial neural networks. PROCLANN retains the simple and elegant declarative semantics of constraint logic programming. Its operational semantics is probabilistic in nature. We show that PROCLANN is sound and weakly complete. A novelty of PROCLANN is that while it is a committedchoice l...
Stolving LargeScale Constraint Satisfaction an Scheduling Problems Using a epair Metho
"... This paper describes a simple heuristic method for solving largescale constraint satisfaction and scheduling problems. Given an initial assignment for the variables in a problem, the method operates by searching though the space of possible repairs. The search is guided by an ordering heuristic, th ..."
Abstract
 Add to MetaCart
This paper describes a simple heuristic method for solving largescale constraint satisfaction and scheduling problems. Given an initial assignment for the variables in a problem, the method operates by searching though the space of possible repairs. The search is guided by an ordering heuristic, the minconflicts heuristic, that attempts to minimize the number of constraint violations after each step. We demonstrate empirically that the method performs orders of magnitude better than traditional backtracking techniques on certain standard problems. For example, the one million queens problem can be solved rapidly using our approach. We also describe practical scheduling applications where the method has been suc. cessfully applied. A theoretical analysis is presented to explain why the method works so well on certain types of problems and to predict when it is likely to be most effective.
Graph Coloring with the Hopfieldclique Network
, 1993
"... We approximately solve, by reduction to Maximum Clique, the graph kcoloring NPhard problem in a binary weights Hopfield net special case. This network was used earlier to approximately solve Maximum Clique and some other NPhard problems by reduction to Maximum Clique. We determine kcoloring appr ..."
Abstract
 Add to MetaCart
(Show Context)
We approximately solve, by reduction to Maximum Clique, the graph kcoloring NPhard problem in a binary weights Hopfield net special case. This network was used earlier to approximately solve Maximum Clique and some other NPhard problems by reduction to Maximum Clique. We determine kcoloring approximation performance on random graphs and on one other distribution of "harder" graphs. We compare our work with earlier work and with theoretical estimates on random graphs. Our optimizing dynamics are discrete and converge in O(number of units) unitswitches. Our broad contribution is in optimizing a new problem in the same binary weights (0/1) network, including the same dynamics, employed earlier for other problems. Our mapping does not admit any invalid solutions and is goodnesspreserving in a formal sense. We also view the act of loading problem instances to be solved as associative memories storage. MAXIMUM CLIQUE. In a graph with undirected edges, a clique is a set of vertices s...