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Interpreting Bayesian Logic Programs
 PROCEEDINGS OF THE WORKINPROGRESS TRACK AT THE 10TH INTERNATIONAL CONFERENCE ON INDUCTIVE LOGIC PROGRAMMING
, 2001
"... Various proposals for combining first order logic with Bayesian nets exist. We introduce the formalism of Bayesian logic programs, which is basically a simplification and reformulation of Ngo and Haddawys probabilistic logic programs. However, Bayesian logic programs are sufficiently powerful to ..."
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Cited by 109 (7 self)
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Various proposals for combining first order logic with Bayesian nets exist. We introduce the formalism of Bayesian logic programs, which is basically a simplification and reformulation of Ngo and Haddawys probabilistic logic programs. However, Bayesian logic programs are sufficiently powerful to represent essentially the same knowledge in a more elegant manner. The elegance is illustrated by the fact that they can represent both Bayesian nets and definite clause programs (as in "pure" Prolog) and that their kernel in Prolog is actually an adaptation of an usual Prolog metainterpreter.
Fixpoint semantics for logic programming  a survey
, 2000
"... The variety of semantical approaches that have been invented for logic programs is quite broad, drawing on classical and manyvalued logic, lattice theory, game theory, and topology. One source of this richness is the inherent nonmonotonicity of its negation, something that does not have close para ..."
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Cited by 106 (0 self)
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The variety of semantical approaches that have been invented for logic programs is quite broad, drawing on classical and manyvalued logic, lattice theory, game theory, and topology. One source of this richness is the inherent nonmonotonicity of its negation, something that does not have close parallels with the machinery of other programming paradigms. Nonetheless, much of the work on logic programming semantics seems to exist side by side with similar work done for imperative and functional programming, with relatively minimal contact between communities. In this paper we summarize one variety of approaches to the semantics of logic programs: that based on fixpoint theory. We do not attempt to cover much beyond this single area, which is already remarkably fruitful. We hope readers will see parallels with, and the divergences from the better known fixpoint treatments developed for other programming methodologies.
Parameter learning of logic programs for symbolicstatistical modeling
 Journal of Artificial Intelligence Research
, 2001
"... We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distributio ..."
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Cited by 92 (19 self)
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We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM algorithm, the graphical EM algorithm, thatrunsfora class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs. The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the BaumWelch algorithm for HMMs, the InsideOutside algorithm for PCFGs, and the one for singly connected Bayesian networks that have beendeveloped independently in each research eld. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can signi cantly outperform the InsideOutside algorithm. 1.
Event calculus reasoning through satisfiability
 Journal of Logic and Computation
, 2004
"... This is a precopyediting, authorproduced PDF of an article accepted for publication in the Journal of Logic and Computation following peer review. The definitive publisherauthenticated version (Mueller, Erik T. (2004). Event calculus reasoning through satisfiability. Journal of Logic and Computa ..."
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Cited by 27 (8 self)
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This is a precopyediting, authorproduced PDF of an article accepted for publication in the Journal of Logic and Computation following peer review. The definitive publisherauthenticated version (Mueller, Erik T. (2004). Event calculus reasoning through satisfiability. Journal of Logic and Computation, 14(5), 703–730.) is available online at:
Constraint Databases: A Survey
 Semantics in Databases, number 1358 in LNCS
, 1998
"... . Constraint databases generalize relational databases by finitely representable infinite relations. This paper surveys the state of the art in constraint databases: known results, remaining open problems and current research directions. The paper also describes a new algebra for databases with inte ..."
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Cited by 23 (3 self)
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. Constraint databases generalize relational databases by finitely representable infinite relations. This paper surveys the state of the art in constraint databases: known results, remaining open problems and current research directions. The paper also describes a new algebra for databases with integer order constraints and a complexity analysis of evaluating queries in this algebra. In memory of Paris C. Kanellakis 1 Introduction There is a growing interest in recent years among database researchers in constraint databases, which are a generalization of relational databases by finitely representable infinite relations. Constraint databases are parametrized by the type of constraint domains and constraint used. The good news is that for many parameters constraint databases leave intact most of the fundamental assumptions of the relational database framework proposed by Codd. In particular, 1. Constraint databases can be queried by constraint query languages that (a) have a semantics ba...
Characterization of Strongly Equivalent Logic Programs in Intermediate Logics
 in Intermediate Logics. Theory and Practice of Logic Programming
, 2001
"... The nonclassical, nonmonotonic inference relation associated with the stable model semantics for logic programs gives rise to a relationship of strong equivalence between logical programs that can be veri ed in the 3valued Godel logic, G3, the strongest nonclassical intermediate proposition ..."
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Cited by 23 (0 self)
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The nonclassical, nonmonotonic inference relation associated with the stable model semantics for logic programs gives rise to a relationship of strong equivalence between logical programs that can be veri ed in the 3valued Godel logic, G3, the strongest nonclassical intermediate propositional logic (see [10]). In this paper we will show that KC (the logic of :p _ ::p), is the weakest intermediate logic for which strongly equivalent logic programs in a language allowing negations are logically equivalent.
The theoretical foundations of LPTP (a logic program theorem prover)
 J. of Logic Programming
, 1998
"... This article contains the theoretical foundations of LPTP, a logic program theorem prover that has been implemented in Prolog by the author. LPTP is an interactive theorem prover in which one can prove correctness properties of pure Prolog programs that contain negation and builtin predicates like ..."
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Cited by 18 (2 self)
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This article contains the theoretical foundations of LPTP, a logic program theorem prover that has been implemented in Prolog by the author. LPTP is an interactive theorem prover in which one can prove correctness properties of pure Prolog programs that contain negation and builtin predicates like is/2 and call/n + 1. The largest example program that has been verified using LPTP is 635 lines long including its specification. The full formal correctness proof is 13128 lines long (133 pages). The formal theory underlying LPTP is the inductive extension of pure Prolog programs. This is a firstorder theory that contains induction principles corresponding to the definition of the predicates in the program plus appropriate axioms for builtin predicates. The inductive extension allows to express modes and types of predicates. These can then be used to prove termination and correctness properties of programs. The main result of this article is that the inductive extension is an adequate axiomatization of the operational semantics of pure Prolog with builtin predicates. Keywords: Verification of logic programs; pure Prolog; lefttermination; induction. 1
Modeling scientific theories as PRISM programs
 IN PROCEEDINGS OF ECAI'98 WORKSHOP ON MACHINE DISCOVERY
, 1998
"... PRISM is a new type of symbolicstatistical modeling language which integrates logic programming and learning seamlessly. It is designed for the symbolicstatistical modeling of complex phenomena such as genetics and economics where logical/social rules and uncertainty interact, thus expected to be ..."
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Cited by 17 (14 self)
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PRISM is a new type of symbolicstatistical modeling language which integrates logic programming and learning seamlessly. It is designed for the symbolicstatistical modeling of complex phenomena such as genetics and economics where logical/social rules and uncertainty interact, thus expected to be a valuable tool for scientific discovery. In this paper, we first give a detailed account of PRISM at propositional logic level. Then we concentrate, instead of looking over various fields, on one subject, the inheritance mechanism of blood types. We show with experimental results that various theories of blood type inheritance are described as PRISM programs. Finally we suggest possible extensions of PRISM. The reader is assumed to be familiar with logic programming [8].
A Guide To The NUProlog Debugging Environment
"... The NUProlog Debugging Environment (Nude) is a collection of integrated tools for locating bugs in both pure and nonlogical NUProlog programs. It has static analyses and userdriven dynamic analyses including a fourport debugger and a declarative debugger. This document is a guide to using the e ..."
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Cited by 16 (1 self)
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The NUProlog Debugging Environment (Nude) is a collection of integrated tools for locating bugs in both pure and nonlogical NUProlog programs. It has static analyses and userdriven dynamic analyses including a fourport debugger and a declarative debugger. This document is a guide to using the environment. Contents 1 introduction 3 1.1 Nude at a glance : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 1.2 limitations of Nude : : : : : : : : : : : : : : : : : : : : : : : : : 4 2 background on debugging 4 2.1 traditional Prolog debugging : : : : : : : : : : : : : : : : : : : : 4 2.2 declarative debugging : : : : : : : : : : : : : : : : : : : : : : : : 5 2.2.1 how does it work? : : : : : : : : : : : : : : : : : : : : : : 5 2.2.2 what problems are there? : : : : : : : : : : : : : : : : : : 5 3 components of Nude 6 3.1 static analyses : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6 3.1.1 Nit : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 3.1.2 th...