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Nonmonotonic Reasoning for FirstOrder Programs
"... . The stable model semantics and the wellfounded semantics are usually considered for programs with rules of the simple structure, which is a consequence of literals. We generalize these semantics for firstorder programs. We proof properties of these semantics, particular relations with minimal mod ..."
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models and equivalent transformations. Besides, we investigate the possibilities of reducing firstorder programs to normal logic programs. We show that the standard approach to firstorder programs based on the LloydTopor transformations is unsatisfactory for nonmonotonic reasoning. 1 Introduction
An InstantiationBased Theorem Prover for FirstOrder Programming
"... Firstorder programming (FOP) is a new representation language that combines the strengths of mixedinteger linear programming (MILP) and firstorder logic (FOL). In this paper we describe a novel feasibility proving system for FOP formulas that combines MILP solving with instancebased methods from ..."
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Cited by 2 (1 self)
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Firstorder programming (FOP) is a new representation language that combines the strengths of mixedinteger linear programming (MILP) and firstorder logic (FOL). In this paper we describe a novel feasibility proving system for FOP formulas that combines MILP solving with instancebased methods
An InstantiationBased Theorem Prover for FirstOrder Programming
"... Firstorder programming (FOP) is a new representation language that combines the strengths of mixedinteger linear programming (MILP) and firstorder logic (FOL). In this paper we describe a novel feasibility proving system for FOP formulas that combines MILP solving with instancebased methods fr ..."
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Firstorder programming (FOP) is a new representation language that combines the strengths of mixedinteger linear programming (MILP) and firstorder logic (FOL). In this paper we describe a novel feasibility proving system for FOP formulas that combines MILP solving with instancebased methods
Interprocedural Control Flow Analysis of FirstOrder Programs with Tail Call Optimization
 In Proceedings of 17th ACM Symposium on Principles of Programming Languages (POPL
, 1996
"... The analysis of control flow Involves figuring out where returns will go. How this may be done With items LR0 and1 Is what in this paper we show. 1 Introduction Most code optimizations depend on control flow analysis, typically expressed in the form of a control flow graph [1]. Traditional algori ..."
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Cited by 9 (0 self)
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boundaries. Determining interprocedural control flow (for firstorder programs) is relatively straightforward in the absence of tail call optimization, since procedures return control to the point immediately after the call. Tail call optimization complicates the analysis because returns may transfer control
On Compilation of HigherOrder Concurrent Programs into First Order Programs Preserving Scope Equivalence
"... Abstract — This paper discusses the expressive power of a graph rewriting model of concurrent processes with higherorder communication. As we reported before, it is difficult to represent the scopes of names using models based on process algebra. Then we presented a model of concurrent systems based ..."
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that there is no compilation mapping from the higherorder model into the firstorder model that is homomorphic wrt input context and full abstract wrt the scope equivalence. As reported, it is possible to compile LHOπ processes into firstorder πcalculus processes preserving a behavioral equivalence. In that sense
The Semantics of Predicate Logic as a Programming Language
 JOURNAL OF THE ACM
, 1976
"... Sentences in firstorder predicate logic can be usefully interpreted as programs In this paper the operational and fixpoint semantics of predicate logic programs are defined, and the connections with the proof theory and model theory of logic are investigated It is concluded that operational seman ..."
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Cited by 808 (18 self)
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Sentences in firstorder predicate logic can be usefully interpreted as programs In this paper the operational and fixpoint semantics of predicate logic programs are defined, and the connections with the proof theory and model theory of logic are investigated It is concluded that operational
Description Logic Programs: Combining Logic Programs with Description Logic
, 2002
"... We show how to interoperate, semantically and inferentially, between the leading Semantic Web approaches to rules (RuleML Logic Programs) and ontologies (OWL/DAML+OIL Description Logic) via analyzing their expressive intersection. To do so, we define a new intermediate knowledge representation (KR) ..."
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Cited by 529 (46 self)
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) contained within this intersection: Description Logic Programs (DLP), and the closely related Description Horn Logic (DHL) which is an expressive fragment of firstorder logic (FOL). DLP provides a significant degree of expressiveness, substantially greater than the RDFSchema fragment of Description Logic.
Learning Stochastic Logic Programs
, 2000
"... Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a firstorder r ..."
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Cited by 1194 (81 self)
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Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a firstorder
Automatically characterizing large scale program behavior
, 2002
"... Understanding program behavior is at the foundation of computer architecture and program optimization. Many programs have wildly different behavior on even the very largest of scales (over the complete execution of the program). This realization has ramifications for many architectural and compile ..."
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Cited by 778 (41 self)
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piler techniques, from thread scheduling, to feedback directed optimizations, to the way programs are simulated. However, in order to take advantage of timevarying behavior, we.must first develop the analytical tools necessary to automatically and efficiently analyze program behavior over large sections
Markov Logic Networks
 MACHINE LEARNING
, 2006
"... We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the ..."
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Cited by 816 (39 self)
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We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects
Results 1  10
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