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101
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.
NonFailure Analysis for Logic Programs
 ACM Transactions on Programming Languages and Systems
, 1997
"... We provide a method whereby, given mode and (upper approximation) type information, we can detect procedures and goals that can be guaranteed to not fail (i.e., to produce at least one solution or not terminate). The technique is based on an intuitively very simple notion, that of a (set of) tests & ..."
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Cited by 135 (14 self)
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We provide a method whereby, given mode and (upper approximation) type information, we can detect procedures and goals that can be guaranteed to not fail (i.e., to produce at least one solution or not terminate). The technique is based on an intuitively very simple notion, that of a (set of) tests "covering" the type of a set of variables. We show that the problem of determining a covering is undecidable in general, and give decidability and complexity results for the Herbrand and linear arithmetic constraint systems. We give sound algorithms for determining covering that are precise and efficient in practice. Based on this information, we show how to identify goals and procedures that can be guaranteed to not fail at runtime. Applications of such nonfailure information include programming error detection, program transformations and parallel execution optimization, avoiding speculative parallelism and estimating lower bounds on the computational costs of goals, which can be used for ...
Fast and Precise Regular Approximation of Logic Programs
, 1993
"... A practical procedure for computing a regular approximation of a logic program is given. Regular approximations are useful in a variety of tasks in debugging, program specialisation and compiletime optimisation. The algorithm shown here incorporates optimisations taken from deductive database fixpo ..."
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Cited by 105 (19 self)
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A practical procedure for computing a regular approximation of a logic program is given. Regular approximations are useful in a variety of tasks in debugging, program specialisation and compiletime optimisation. The algorithm shown here incorporates optimisations taken from deductive database fixpoint algorithms and efficient bottomup abstract interpretation techniques. Frameworks for defining regular approximations have been put forward in the past, but the emphasis has usually been on theoretical aspects. Our results contribute mainly to the development of effective analysis tools that can be applied to large programs. Precision of the approximation can be greatly improved by applying queryanswer transformations to a program and a goal, thus capturing some argument dependency information. A novel technique is to use transformations based on computation rules other than lefttoright to improve precision further. We give performance results for our procedure on a range of programs. 1
Solving systems of set constraints
 In Symposium on Logic in Computer Science
, 1992
"... Systems of set constraints are a natural formalism for many problems in program analysis. Set constraints are also a generalization of tree automata. We present an algorithm for solving systems of set constraints built from free variables, constructors, and the set operations of intersection, uni ..."
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Cited by 83 (12 self)
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Systems of set constraints are a natural formalism for many problems in program analysis. Set constraints are also a generalization of tree automata. We present an algorithm for solving systems of set constraints built from free variables, constructors, and the set operations of intersection, union, and complement. Furthermore, we show that all solutions of such systems can be nitely represented. 1 1
Formal Language, Grammar and SetConstraintBased Program Analysis by Abstract Interpretation
, 1995
"... Grammarbased program analysis à la Jones and Muchnick and setconstraintbased program analysis à la Aiken and Heintze are static analysis techniques that have traditionally been seen as quite different from abstractinterpretationbased analyses, in particular because of their apparent noniterati ..."
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Cited by 81 (10 self)
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Grammarbased program analysis à la Jones and Muchnick and setconstraintbased program analysis à la Aiken and Heintze are static analysis techniques that have traditionally been seen as quite different from abstractinterpretationbased analyses, in particular because of their apparent noniterative nature. For example, on page 18 of N. Heintze thesis, it is alleged that ``The finitary nature of abstract interpretation implies that there is a fundamental limitation on the accuracy of this approach to program analysis. There are decidable kinds of analysis that cannot be computed using abstract interpretation (even with widening and narrowing). The setbased analysis considered in this thesis is one example''. On the contrary, we show that grammar and setconstraintbased program analyses are similar abstract interpretations with iterative fixpoint computation using either a widening or a finitary grammar/setconstraints transformer or even a finite domain for each particular program. The understanding of grammarbased and setconstraintbased program analysis as a particular instance of abstract interpretation of a semantics has several advantages. First, the approximation process is formalized and not only explained using examples. Second, a domain of abstract properties is exhibited which is of general scope. Third, these analyses can be easily combined with other abstractinterpretationbased analyses, in particular for the analysis of numerical values. Fourth, they can be generalized to very powerful attributedependent and contextdependent analyses. Finally, a few misunderstandings may be removed.
On the Complexity Analysis of Static Analyses
 Journal of the ACM
, 1999
"... . This paper argues that for many algorithms, and static analysis ..."
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Cited by 75 (3 self)
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. This paper argues that for many algorithms, and static analysis
Set Constraints: Results, Applications and Future Directions
 In Second Workshop on the Principles and Practice of Constraint Programming
"... . Set constraints are a natural formalism for many problems that arise in program analysis. This paper provides a brief introduction to set constraints: what set constraints are, why they are interesting, the current state of the art, open problems, applications and implementations. 1 Introduction ..."
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Cited by 74 (4 self)
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. Set constraints are a natural formalism for many problems that arise in program analysis. This paper provides a brief introduction to set constraints: what set constraints are, why they are interesting, the current state of the art, open problems, applications and implementations. 1 Introduction Set constraints are a natural formalism for describing relationships between sets of terms of a free algebra. A set constraint has the form X ` Y , where X and Y are set expressions. Examples of set expressions are 0 (the empty set), ff (a setvalued variable), c(X; Y ) (a constructor application), and the union, intersection, or complement of set expressions. Recently, there has been a great deal of interest in program analysis algorithms based on solving systems of set constraints, including analyses for functional languages [AWL94, Hei94, AW93, AM91, JM79, MR85, Rey69], logic programming languages [AL94, HJ92, HJ90b, Mis84], and imperative languages [HJ91]. In these algorithms, sets of...
The complexity of set constraints
, 1993
"... Set constraints are relations between sets of terms. They have been used extensively in various applications in program analysis and type inference. We present several results on the computational complexity of solving systems of set constraints. The systems we study form a natural complexity hierar ..."
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Cited by 74 (11 self)
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Set constraints are relations between sets of terms. They have been used extensively in various applications in program analysis and type inference. We present several results on the computational complexity of solving systems of set constraints. The systems we study form a natural complexity hierarchy depending on the form of the constraint language.
Set Constraints are the Monadic Class
, 1992
"... We investigate the relationship between set constraints and the monadic class of firstorder formulas and show that set constraints are essentially equivalent to the monadic class. From this equivalence we can infer that the satisfiability problem for set constraints is complete for NEXPTIME. Mor ..."
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Cited by 72 (0 self)
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We investigate the relationship between set constraints and the monadic class of firstorder formulas and show that set constraints are essentially equivalent to the monadic class. From this equivalence we can infer that the satisfiability problem for set constraints is complete for NEXPTIME. More precisely, we prove that this problem has a lower bound of NTIME(c n= log n ). The relationship between set constraints and the monadic class also gives us decidability and complexity results for certain practically useful extensions of set constraints, in particular "negative projections" and subterm equality tests.
Static Type Inference in a Dynamically Typed Language
 In Eighteenth Annual ACM Symposium on Principles of Programming Languages
, 1991
"... We present a type inference system for FL based on an operational, rather than a denotational, formulation of types. The essential elements of the system are a type language based on regular trees and a type inference logic that implements an abstract interpretation of the operational semantics of F ..."
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Cited by 66 (8 self)
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We present a type inference system for FL based on an operational, rather than a denotational, formulation of types. The essential elements of the system are a type language based on regular trees and a type inference logic that implements an abstract interpretation of the operational semantics of FL. We use a nonstandard approach to type inference because our requirementsusing type information in the optimization of functional programsdiffer substantially from those of other type systems. 1 Introduction Compilers derive at least two benefits from static type inference: the ability to detect and report potential runtime errors at compiletime, and the use of type information in program optimization. Traditionally, type systems have emphasized the detection of type errors. Statically typed functional languages such as Haskell [HWA*88] and ML [HMT89] include type constraints as part of the language definition, making some type inference necessary to ensure that type constraints ...