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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 811 (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
Learning logical definitions from relations
 MACHINE LEARNING
, 1990
"... This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas that have proved effective in attributevalue learning systems, but extends them to a firstorder formalism. This new system has been applied successfully to several tasks taken fro ..."
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Cited by 930 (8 self)
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This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas that have proved effective in attributevalue learning systems, but extends them to a firstorder formalism. This new system has been applied successfully to several tasks taken
Inverse entailment and Progol
, 1995
"... This paper firstly provides a reappraisal of the development of techniques for inverting deduction, secondly introduces ModeDirected Inverse Entailment (MDIE) as a generalisation and enhancement of previous approaches and thirdly describes an implementation of MDIE in the Progol system. Progol ..."
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Cited by 721 (61 self)
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is implemented in C and available by anonymous ftp. The reassessment of previous techniques in terms of inverse entailment leads to new results for learning from positive data and inverting implication between pairs of clauses.
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 1181 (79 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 first
AgentSpeak(L): BDI Agents speak out in a logical computable language
, 1996
"... BeliefDesireIntention (BDI) agents have been investigated by many researchers from both a theoretical specification perspective and a practical design perspective. However, there still remains a large gap between theory and practice. The main reason for this has been the complexity of theoremprov ..."
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Cited by 509 (2 self)
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theoretic semantics of a language AgentSpeak(L). This language can be viewed as an abstraction of one of the implemented BDI systems (i.e., PRS) and allows agent programs to be written and interpreted in a manner similar to that of hornclause logic programs. We show how to perform derivations in this logic using a
Learning and development in neural networks: The importance of starting small
 Cognition
, 1993
"... It is a striking fact that in humans the greatest learnmg occurs precisely at that point in time childhood when the most dramatic maturational changes also occur. This report describes possible synergistic interactions between maturational change and the ability to learn a complex domain (language ..."
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Cited by 518 (18 self)
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(language), as investigated in connectionist networks. The networks are trained to process complex sentences involving relative clauses, number agreement, and several types of verb argument structure. Training fails in the case of networks which are fully formed and ‘adultlike ’ in their capacity. Training
The Semantics of Predicate Logic as a Programming Language
 Journal of the ACM
, 1976
"... ABSTRACT Sentences in firstorder predicate logic can be usefully interpreted as programs In this paper the operational and fixpomt 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 ..."
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Cited by 810 (18 self)
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ABSTRACT Sentences in firstorder predicate logic can be usefully interpreted as programs In this paper the operational and fixpomt 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 semantics is a part of proof theory and that fixpolnt semantics is a special case of modeltheoret:c semantics KEY WORDS AND PHRASES predicate logic as a programming language, semantics of programming languages, resolution theorem proving, operaUonal versus denotatlonal semantics, SLresoluuon, flxpomt characterization
A New Method for Solving Hard Satisfiability Problems
 AAAI
, 1992
"... We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approac ..."
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Cited by 734 (21 self)
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We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approaches such as the DavisPutnam procedure or resolution. We also show that GSAT can solve structured satisfiability problems quickly. In particular, we solve encodings of graph coloring problems, Nqueens, and Boolean induction. General application strategies and limitations of the approach are also discussed. GSAT is best viewed as a modelfinding procedure. Its good performance suggests that it may be advantageous to reformulate reasoning tasks that have traditionally been viewed as theoremproving problems as modelfinding tasks.
The Lorel Query Language for Semistructured Data
 International Journal on Digital Libraries
, 1997
"... We present the Lorel language, designed for querying semistructured data. Semistructured data is becoming more and more prevalent, e.g., in structured documents such as HTML and when performing simple integration of data from multiple sources. Traditional data models and query languages are inapprop ..."
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Cited by 734 (29 self)
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We present the Lorel language, designed for querying semistructured data. Semistructured data is becoming more and more prevalent, e.g., in structured documents such as HTML and when performing simple integration of data from multiple sources. Traditional data models and query languages are inappropriate, since semistructured data often is irregular, some data is missing, similar concepts are represented using different types, heterogeneous sets are present, or object structure is not fully known. Lorel is a userfriendly language in the SQL/OQL style for querying such data effectively. For wide applicability, the simple object model underlying Lorel can be viewed as an extension of ODMG and the language as an extension of OQL. The main novelties of the Lorel language are: (i) extensive use of coercion to relieve the user from the strict typing of OQL, which is inappropriate for semistructured data
Results 1  10
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