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294,853
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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order rangerestricted definite clause. This paper summarises the syntax, distributional semantics and proof techniques for SLPs and then discusses how a standard Inductive Logic Programming (ILP) system, Progol, has been modied to support learning of SLPs. The resulting system 1) nds an SLP with uniform
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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learned from relational databases by iteratively optimizing a pseudolikelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a realworld database and knowledge base in a university domain illustrate the promise of this approach.
BottomUp Relational Learning of Pattern Matching Rules for Information Extraction
, 2003
"... Information extraction is a form of shallow text processing that locates a specified set of relevant items in a naturallanguage document. Systems for this task require significant domainspecific knowledge and are timeconsuming and difficult to build by hand, making them a good application for ..."
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Cited by 406 (20 self)
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for machine learning. We present an algorithm, RAPIER, that uses pairs of sample documents and filled templates to induce patternmatch rules that directly extract fillers for the slots in the template. RAPIER is a bottomup learning algorithm that incorporates techniques from several inductive logic
Strongly Equivalent Logic Programs
 ACM Transactions on Computational Logic
, 2000
"... A logic program 1 is said to be equivalent to a logic program 2 in the sense of the answer set semantics if 1 and 2 have the same answer sets. We are interested in the following stronger condition: for every logic program , 1 [ has the same answer sets as 2 [ . The study of strong equival ..."
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Cited by 231 (36 self)
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equivalence is important, because we learn from it how one can simplify a part of a logic program without looking at the rest of it. The main theorem shows that the verication of strong equivalence can be accomplished by checking the equivalence of formulas in a monotonic logic, called the logic of here
Spawn: A Distributed Computational Economy
, 1991
"... Carl A. Waldspurger. Priority Flow: A framework for abstract, adaptive resource management. MIT LCS Parallel Software Group, Internal Memo (unpublished), May 1990. 32 [13] Carl Hewitt. The challenge of open systems. Byte, 10:223 242, April 1985. [14] Bernardo A. Huberman and Tad Hogg. The behavio ..."
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Cited by 293 (14 self)
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Holland, Amsterdam, 1988. [16] Kenneth M. Kahn and Vijay A. Saraswat. Money as a concurrent logic program. Technical report, Xerox PARC, 1989. [17] Jeffrey O. Kephart, Tad Hogg, and Bernardo A. Huberman. Dynamics of computational ecosystems. Physical Review A, 40:404 421, 1989. [18] Douglas B. Lenat. The role
Learning to Parse Database Queries Using Inductive Logic Programming
 In Proceedings of the Thirteenth National Conference on Artificial Intelligence
, 1996
"... This paper presents recent work using the Chill parser acquisition system to automate the construction of a naturallanguage interface for database queries. Chill treats parser acquisition as the learning of searchcontrol rules within a logic program representing a shiftreduce parser and uses tec ..."
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Cited by 156 (22 self)
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This paper presents recent work using the Chill parser acquisition system to automate the construction of a naturallanguage interface for database queries. Chill treats parser acquisition as the learning of searchcontrol rules within a logic program representing a shiftreduce parser and uses
LEARNING AND FORGETTING
"... Researchers of industrial relations issues in manufacturing have long recognized that careful study of production has significant implications for laborproductivity. Recent theory and analysis has shown the large influence of organizational forgetting. The authors of this study demonstrate that forg ..."
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Researchers of industrial relations issues in manufacturing have long recognized that careful study of production has significant implications for laborproductivity. Recent theory and analysis has shown the large influence of organizational forgetting. The authors of this study demonstrate
The Role of Forgetting in Learning
 In Proceedings of the Fifth International Conference on Machine Learning
, 1988
"... This paper is a discussion of the relationship between learning and forgetting. An analysis of the economics of learning is carried out and it is argued that knowledge can sometimes have a negative value. A series of experiments involving a program which learns to traverse state spaces is described. ..."
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Cited by 42 (3 self)
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This paper is a discussion of the relationship between learning and forgetting. An analysis of the economics of learning is carried out and it is argued that knowledge can sometimes have a negative value. A series of experiments involving a program which learns to traverse state spaces is described
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 122 (20 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
A Theory of Forgetting in Logic Programming ∗
"... The study of forgetting for reasoning has attracted considerable attention in AI. However, much of the work on forgetting, and other related approaches such as independence, irrelevance and novelty, has been restricted to the classical logics. This paper describes a detailed theoretical investigatio ..."
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investigation of the notion of forgetting in the context of logic programming. We first provide a semantic definition of forgetting under the answer sets for extended logic programs. We then discuss the desirable properties and some motivating examples. An important result of this study is an algorithm
Results 1  10
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