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727
Knowledge compilation and theory approximation
 Journal of the ACM
, 1996
"... Computational efficiency is a central concern in the design of knowledge representation systems. In order to obtain efficient systems, it has been suggested that one should limit the form of the statements in the knowledge base or use an incomplete inference mechanism. The former approach is often t ..."
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Cited by 187 (5 self)
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Computational efficiency is a central concern in the design of knowledge representation systems. In order to obtain efficient systems, it has been suggested that one should limit the form of the statements in the knowledge base or use an incomplete inference mechanism. The former approach is often too restrictive for practical applications, whereas the latter leads to uncertainty about exactly what can and cannot be inferred from the knowledge base. We present a third alternative, in which knowledge given in a general representation language is translated (compiled) into a tractable form — allowing for efficient subsequent query answering. We show how propositional logical theories can be compiled into Horn theories that approximate the original information. The approximations bound the original theory from below and above in terms of logical strength. The procedures are extended to other tractable languages (for example, binary clauses) and to the firstorder case. Finally, we demonstrate the generality of our approach by compiling concept descriptions in a general framebased language into a tractable form.
A Survey of Automated Timetabling
, 1999
"... The timetabling problem consists in scheduling a sequence of lectures between teachers and students in a prefixed period of time (typically a week), satisfying a set of constraints of various types. A large number of variants of the timetabling problem have been proposed in the literature, which d ..."
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Cited by 185 (15 self)
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The timetabling problem consists in scheduling a sequence of lectures between teachers and students in a prefixed period of time (typically a week), satisfying a set of constraints of various types. A large number of variants of the timetabling problem have been proposed in the literature, which differ from each other based on the type of institution involved (university or school) and the type of constraints. This problem, that has been traditionally considered in the operational research field, has recently been tackled with techniques belonging also to Artificial Intelligence (e.g., genetic algorithms, tabu search, and constraint satisfaction). In this paper, we survey the various formulations of the problem, and the techniques and algorithms used for its solution.
Optimistic parallelism requires abstractions
 In PLDI
, 2007
"... Irregular applications, which manipulate large, pointerbased data structures like graphs, are difficult to parallelize manually. Automatic tools and techniques such as restructuring compilers and runtime speculative execution have failed to uncover much parallelism in these applications, in spite o ..."
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Cited by 176 (24 self)
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Irregular applications, which manipulate large, pointerbased data structures like graphs, are difficult to parallelize manually. Automatic tools and techniques such as restructuring compilers and runtime speculative execution have failed to uncover much parallelism in these applications, in spite of a lot of effort by the research community. These difficulties have even led some researchers to wonder if there is any coarsegrain parallelism worth exploiting in irregular applications. In this paper, we describe two realworld irregular applications: a Delaunay mesh refinement application and a graphics application that performs agglomerative clustering. By studying the algorithms and data structures used in these applications, we show that there is substantial coarsegrain, data parallelism in these applications, but that this parallelism is very dependent on the input data and therefore cannot be uncovered by compiler analysis. In principle, optimistic techniques such as threadlevel speculation can be used to uncover this parallelism, but we argue that current implementations cannot accomplish this because they do not use the proper abstractions for the data structures in these programs. These insights have informed our design of the Galois system, an objectbased optimistic parallelization system for irregular applications. There are three main aspects to Galois: (1) a small number of syntactic constructs for packaging optimistic parallelism as iteration over ordered and unordered sets, (2) assertions about methods in class libraries, and (3) a runtime scheme for detecting and recovering from potentially unsafe accesses to shared memory made by an optimistic computation. We show that Delaunay mesh generation and agglomerative clustering can be parallelized in a straightforward way using the Galois approach, and we present experimental measurements to show that this approach is practical. These results suggest that Galois is a practical approach to exploiting data parallelism in irregular programs.
Improvements To Propositional Satisfiability Search Algorithms
, 1995
"... ... quickly across a wide range of hard SAT problems than any other SAT tester in the literature on comparable platforms. On a Sun SPARCStation 10 running SunOS 4.1.3 U1, POSIT can solve hard random 400variable 3SAT problems in about 2 hours on the average. In general, it can solve hard nvariable ..."
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Cited by 174 (0 self)
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... quickly across a wide range of hard SAT problems than any other SAT tester in the literature on comparable platforms. On a Sun SPARCStation 10 running SunOS 4.1.3 U1, POSIT can solve hard random 400variable 3SAT problems in about 2 hours on the average. In general, it can solve hard nvariable random 3SAT problems with search trees of size O(2 n=18:7 ). In addition to justifying these claims, this dissertation describes the most significant achievements of other researchers in this area, and discusses all of the widely known general techniques for speeding up SAT search algorithms. It should be useful to anyone interested in NPcomplete problems or combinatorial optimization in general, and it should be particularly useful to researchers in either Artificial Intelligence or Operations Research.
An Algorithm to Evaluate Quantified Boolean Formulae and its Experimental Evaluation
 Journal of Automated Reasoning
, 1999
"... The high computational complexity of advanced reasoning tasks such as reasoning about knowledge and planning calls for efficient and reliable algorithms for reasoning problems harder than NP. In this paper we propose Evaluate, an algorithm for evaluating Quantified Boolean Formulae, a language that ..."
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Cited by 154 (4 self)
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The high computational complexity of advanced reasoning tasks such as reasoning about knowledge and planning calls for efficient and reliable algorithms for reasoning problems harder than NP. In this paper we propose Evaluate, an algorithm for evaluating Quantified Boolean Formulae, a language that extends propositional logic in a way such that many advanced forms of propositional reasoning, e.g., circumscription, can be easily formulated as evaluation of a QBF. Algorithms for evaluation of QBFs are suitable for the experimental analysis on a wide range of complexity classes, a property not easily found in other formalisms. Evaluate is based on a generalization of the DavisPutnam procedure for SAT, and is guaranteed to work in polynomial space. Before presenting the algorithm, we discuss several abstract properties of QBFs that we singled out to make it more efficient. We also discuss various options that were investigated about heuristics and data structures, and report the main res...
Experimental Results on the Crossover Point in Random 3sat
 Artificial Intelligence
, 1996
"... Determining whether a propositional theory is satisfiable is a prototypical example of an NPcomplete problem. Further, a large number of problems that occur in knowledgerepresentation, learning, planning, and other ares of AI are essentially satisfiability problems. This paper reports on the most ..."
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Cited by 145 (6 self)
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Determining whether a propositional theory is satisfiable is a prototypical example of an NPcomplete problem. Further, a large number of problems that occur in knowledgerepresentation, learning, planning, and other ares of AI are essentially satisfiability problems. This paper reports on the most extensive set of experiments to date on the location and nature of the crossover point in satisfiability problems. These experiments generally confirm previous results with two notable exceptions. First, we have found that neither of the functions previously proposed accurately models the location of the crossover point. Second, we have found no evidence of any hard problems in the underconstrained region. In fact the hardest problems found in the underconstrained region were many times easier than the easiest unsatisfiable problems found in the neighborhood of the crossover point. We offer explanations for these apparent contradictions of previous results. This work has been supported ...
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 144 (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...
SATzilla: Portfoliobased Algorithm Selection for SAT
"... It has been widely observed that there is no single “dominant ” SAT solver; instead, different solvers perform best on different instances. Rather than following the traditional approach of choosing the best solver for a given class of instances, we advocate making this decision online on a perinst ..."
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Cited by 139 (22 self)
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It has been widely observed that there is no single “dominant ” SAT solver; instead, different solvers perform best on different instances. Rather than following the traditional approach of choosing the best solver for a given class of instances, we advocate making this decision online on a perinstance basis. Building on previous work, we describe SATzilla, an automated approach for constructing perinstance algorithm portfolios for SAT that use socalled empirical hardness models to choose among their constituent solvers. This approach takes as input a distribution of problem instances and a set of component solvers, and constructs a portfolio optimizing a given objective function (such as mean runtime, percent of instances solved, or score in a competition). The excellent performance of our SATzilla portfolios has been independently verified in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one silver and one bronze medal. In this article, we go well beyond SATzilla07 by making the portfolio construction scalable and completely automated, and improving it by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances. We demonstrate the effectiveness of these new techniques in extensive experimental results on data sets including instances from the most recent SAT competition. 1.
Fast Decoding and Optimal Decoding for Machine Translation
 In Proceedings of ACL 39
, 2001
"... A good decoding algorithm is critical ..."
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Towards an understanding of hillclimbing procedures for SAT
 In Proceedings of AAAI93
, 1993
"... Recently several local hillclimbing procedures for propositional satisability havebeen proposed, which are able to solve large and di cult problems beyond the reach ofconventional algorithms like DavisPutnam. By the introduction of some new variants of these procedures, we provide strong experimen ..."
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Cited by 137 (6 self)
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Recently several local hillclimbing procedures for propositional satisability havebeen proposed, which are able to solve large and di cult problems beyond the reach ofconventional algorithms like DavisPutnam. By the introduction of some new variants of these procedures, we provide strong experimental evidence to support the conjecture that neither greediness nor randomness is important in these procedures. One of the variants introduced seems to o er signi cant improvements over earlier procedures. In addition, we investigate experimentally how their performance depends on their parameters. Our results suggest that runtime scales less than simply exponentially in the problem size. 1