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108
Learning while searching in constraintsatisfaction-problems
- In Proceedings AAAI’86
, 1986
"... The popular use of backtracking as a control strategy for theorem proving in PROLOG and in Truth-Maintenance-Systems (TMS) led to increased interest in various schemes for enhancing the efficiency of backtrack search. Researchers have referred to these enhancement schemes by the names ‘ ‘Intelligent ..."
Abstract
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Cited by 27 (0 self)
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‘ ‘Intelligent Backtracking ’ ’ (in PROLOG), ‘ ‘Dependency-directed-backtracking ” (in TMS) and others. Those improve-ments center on the issue of “jumping-back ” to the source of the problem in front of dead-end situations. This paper examines another issue (much less explored) which arises in dead
Algorithms for Constraint-Satisfaction Problems: A Survey
, 1992
"... A large number of problems in AI and other areas of computer science can be viewed as special cases of the constraint-satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, the planning of genetic experiments, an ..."
Abstract
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Cited by 449 (0 self)
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A large number of problems in AI and other areas of computer science can be viewed as special cases of the constraint-satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, the planning of genetic experiments
GSAT and Dynamic Backtracking
- Journal of Artificial Intelligence Research
, 1994
"... There has been substantial recent interest in two new families of search techniques. One family consists of nonsystematic methods such as gsat; the other contains systematic approaches that use a polynomial amount of justification information to prune the search space. This paper introduces a new te ..."
Abstract
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Cited by 386 (15 self)
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that guarantee that this database will be polynomial in the size of the problem in question. 1 INTRODUCTION The past few years have seen rapid progress in the development of algorithms for solving constraintsatisfaction problems, or csps. Csps arise naturally in subfields of AI from planning to vision
The anatomy of easy problems: a constraint-satisfaction formulation
- Proceedings ofIJCAl-85
, 1985
"... This work aims towards the automatic generation of advice to guide the solution of difficult constraintsatisfaction problems (CSPs). The advice is generated by consulting relaxed, easy models which are backtrackfree. We identify a subset of CSPs whose syntactic and semantic properties make them easy ..."
Abstract
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Cited by 5 (2 self)
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This work aims towards the automatic generation of advice to guide the solution of difficult constraintsatisfaction problems (CSPs). The advice is generated by consulting relaxed, easy models which are backtrackfree. We identify a subset of CSPs whose syntactic and semantic properties make them
The 50% Point in Constraint-Satisfaction Problems
, 1995
"... A beautifully simple linear relationship has been observed for the 50 percent solubility point in random 3-SAT problems. We show that a similar relationship holds for binary constraint satisfaction problems. We are able to do this by a detailed re-examination of data published by other authors. 2 D ..."
Abstract
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Cited by 1 (0 self)
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A beautifully simple linear relationship has been observed for the 50 percent solubility point in random 3-SAT problems. We show that a similar relationship holds for binary constraint satisfaction problems. We are able to do this by a detailed re-examination of data published by other authors. 2
SomePracticable FilteringTechniques for the ConstraintSatisfaction Problem
, 1997
"... Filteringtechniques are essential in order to efficiently solve constraintsatisfaction problems (CSPs). A blindsearchoften leads to a combinatorial explosion, the algorithm repeatedly findingthesame local inconsistencies. Maintaining a local consistency can strongly reduce the search effort es ..."
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Filteringtechniques are essential in order to efficiently solve constraintsatisfaction problems (CSPs). A blindsearchoften leads to a combinatorial explosion, the algorithm repeatedly findingthesame local inconsistencies. Maintaining a local consistency can strongly reduce the search effort
An Algorithm for Solving Constraint-Satisfaction Problems
"... We introduce a new method, called constraint-directed-generate-and-test (CDGT ), for solving constraint satisfaction problems (CSPs). Instead of appending a new instantiation of one variable to a partial solution, as in traditional search methods, CDGT joins two sets of partial solutions and then ch ..."
Abstract
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Cited by 1 (0 self)
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We introduce a new method, called constraint-directed-generate-and-test (CDGT ), for solving constraint satisfaction problems (CSPs). Instead of appending a new instantiation of one variable to a partial solution, as in traditional search methods, CDGT joins two sets of partial solutions
An Algorithm for Solving Constraint-Satisfaction Problems
"... We introduce a new method, called constraint-directed-generate-and-test (CDGT), for solving constraint satisfaction problems (CSPs). Instead of appending a new instantiation of one variable to a partial solution, as in traditional search methods, CDGT joins two sets of partial solutions and then che ..."
Abstract
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We introduce a new method, called constraint-directed-generate-and-test (CDGT), for solving constraint satisfaction problems (CSPs). Instead of appending a new instantiation of one variable to a partial solution, as in traditional search methods, CDGT joins two sets of partial solutions
Constraint-Satisfaction Inference for Entity Recognition
"... Abstract One approach to QA answering is to match a question to candidate answers in a background corpus based on semantic overlap, possibly in combination with other levels of matching, such as lexical vector space similarity and syntactic similarity. While the computation of deep semantic similari ..."
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. This problem, like many others in Natural Language Processing, is a sequence labelling task. We describe the development of a new approach to sequence labelling in general, based on the constraint satisfaction inference. The output of the machine-learning-based classifiers that solve aspects of the task (such
Efficient Constraint-Satisfaction in Domains with Time
"... Satisfiability (SAT) testing methods have been used effectively in many inference, planning and constraint satisfaction tasks and thus have been considered a contribution towards artificial general intelligence. However, since SAT constraints are defined over atomic propositions, domains with state ..."
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variables that change over time can lead to extremely large search spaces. This poses both memory- and time-efficiency problems for existing SAT algorithms. In this paper, we propose to address these problems by introducing a language that encodes the temporal intervals over which relations occur
Results 1 - 10
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108
