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40
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 631 (59 self)
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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 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.
Separateandconquer rule learning
 Artificial Intelligence Review
, 1999
"... This paper is a survey of inductive rule learning algorithms that use a separateandconquer strategy. This strategy can be traced back to the AQ learning system and still enjoys popularity as can be seen from its frequent use in inductive logic programming systems. We will put this wide variety of ..."
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Cited by 135 (29 self)
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This paper is a survey of inductive rule learning algorithms that use a separateandconquer strategy. This strategy can be traced back to the AQ learning system and still enjoys popularity as can be seen from its frequent use in inductive logic programming systems. We will put this wide variety of algorithms into a single framework and analyze them along three different dimensions, namely their search, language and overfitting avoidance biases.
First order jkclausal theories are PAClearnable
 Artificial Intelligence
, 1994
"... We present positive PAClearning results for the nonmonotonic inductive logic programming setting. In particular, we show that first order rangerestricted clausal theories that consist of clauses with up to k literals of size at most j each are polynomialsample polynomialtime PAClearnable with on ..."
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Cited by 64 (27 self)
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We present positive PAClearning results for the nonmonotonic inductive logic programming setting. In particular, we show that first order rangerestricted clausal theories that consist of clauses with up to k literals of size at most j each are polynomialsample polynomialtime PAClearnable with onesided error from positive examples only. In our framework, concepts are clausal theories and examples are finite interpretations. We discuss the problems encountered when learning theories which only have infinite nontrivial models and propose a way to avoid these problems using a representation change called flattening. Finally, we compare our results to PAClearnability results for the normal inductive logic programming setting. 1
Inductive Logic Programming: derivations, successes and shortcomings
 SIGART Bulletin
, 1993
"... Inductive Logic Programming (ILP) is a research area which investigates the construction of firstorder definite clause theories from examples and background knowledge. ILP systems have been applied successfully in a number of realworld domains. These include the learning of structureactivity rules ..."
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Cited by 31 (3 self)
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Inductive Logic Programming (ILP) is a research area which investigates the construction of firstorder definite clause theories from examples and background knowledge. ILP systems have been applied successfully in a number of realworld domains. These include the learning of structureactivity rules for drug design, finiteelement mesh design rules, rules for primarysecondary prediction of protein structure and fault diagnosis rules for satellites. There is a well established tradition of learninginthelimit results in ILP. Recently some results within Valiant's PAClearning framework have also been demonstrated for ILP systems. In this paper it is argued that algorithms can be directly derived from the formal specifications of ILP. This provides a common basis for Inverse Resolution, ExplanationBased Learning, Abduction and Relative Least General Generalisation. A new generalpurpose, efficient approach to predicate invention is demonstrated. ILP is underconstrained by its logical ...
Inverting Implication
 Artificial Intelligence Journal
, 1992
"... All generalisations within logic involve inverting implication. Yet, ever since Plotkin's work in the early 1970's methods of generalising firstorder clauses have involved inverting the clausal subsumption relationship. However, even Plotkin realised that this approach was incomplete. Since inversi ..."
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Cited by 26 (2 self)
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All generalisations within logic involve inverting implication. Yet, ever since Plotkin's work in the early 1970's methods of generalising firstorder clauses have involved inverting the clausal subsumption relationship. However, even Plotkin realised that this approach was incomplete. Since inversion of subsumption is central to many Inductive Logic Programming approaches, this form of incompleteness has been propagated to techniques such as Inverse Resolution and Relative Least General Generalisation. A more complete approach to inverting implication has been attempted with some success recently by Lapointe and Matwin. In the present paper the author derives general solutions to this problem from first principles. It is shown that clausal subsumption is only incomplete for selfrecursive clauses. Avoiding this incompleteness involves algorithms which find "nth roots" of clauses. Completeness and correctness results are proved for a nondeterministic algorithms which constructs nth ro...
Which Hypotheses Can Be Found with Inverse Entailment? (Extended Abstract)
, 1997
"... 3  Akihiro YAMAMOTO y Fachgebiet Intellektik, Fachbereich Informatik Technische Hochschule Darmstadt Alexanderstr. 10, D64283 Darmstadt, GERMANY Phone : +496151162863, Fax : +496151165326 Email : yamamoto@intellektik.informatik.thdarmstadt.de Abstract In this paper we give a completene ..."
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Cited by 22 (2 self)
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3  Akihiro YAMAMOTO y Fachgebiet Intellektik, Fachbereich Informatik Technische Hochschule Darmstadt Alexanderstr. 10, D64283 Darmstadt, GERMANY Phone : +496151162863, Fax : +496151165326 Email : yamamoto@intellektik.informatik.thdarmstadt.de Abstract In this paper we give a completeness theorem of an inductive inference rule inverse entailment proposed by Muggleton. Our main result is that a hypothesis clause H can be derived from an example E under a background theory B with inverse entailment iff H subsumes E relative to B in Plotkin's sense. The theory B can be any clausal theory, and the example E can be any clause which is neither a tautology nor implied by B. The derived hypothesis H is a clause which is not always definite. In order to prove the result we give declarative semantics for arbitrary consistent clausal theories, and show that SBresolution, which was originally introduced by Plotkin, is complete procedural semantics. The completeness is shown as ...
Scaling Up ILP to Large Examples: Results on Link Discovery for CounterTerrorism
 In KDD03 Workshop on MultiRelational Data Mining
, 2003
"... Inductive Logic Programming (ILP) has been shown to be a viable approach to many problems in multirelational data mining (e.g. ..."
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Cited by 9 (1 self)
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Inductive Logic Programming (ILP) has been shown to be a viable approach to many problems in multirelational data mining (e.g.
Relational IBL in music with a new structural similarity measure
 In Proceedings of the International Conference on Inductive Logic Programming
, 2003
"... Abstract. It is well known that many hard tasks considered in machine learning and data mining can be solved in an rather simple and robust way with an instance and distancebased approach. In this paper we present another difficult task: learning, from large numbers of performances by concert pian ..."
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Cited by 8 (2 self)
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Abstract. It is well known that many hard tasks considered in machine learning and data mining can be solved in an rather simple and robust way with an instance and distancebased approach. In this paper we present another difficult task: learning, from large numbers of performances by concert pianists, to play music expressively. We model the problem as a multilevel decomposition and prediction task. Motivated by structural characteristics of such a task, we propose a new relational distance measure that is a rather straightforward combination of two existing measures. Empirical evaluation shows that our approach is in general viable and our algorithm, named DISTALL, is indeed able to produce musically interesting results. The experiments also provide evidence of the success of ILP in a complex domain such as music performance: it is shown that our instancebased learner operating on structured, relational data outperforms a propositional kNN algorithm.
Learning Range Restricted Horn Expressions
, 1999
"... . We study the learnability of first order Horn expressions from equivalence and membership queries. We show that the class of range restricted Horn expressions, where every term in the consequent of every clause appears also in the antecedent of the clause, is learnable. The result holds both for t ..."
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Cited by 6 (4 self)
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. We study the learnability of first order Horn expressions from equivalence and membership queries. We show that the class of range restricted Horn expressions, where every term in the consequent of every clause appears also in the antecedent of the clause, is learnable. The result holds both for the model where interpretations are examples (learning from interpretations) and the model where clauses are examples (learning from entailment). The paper utilises a previous result on learning function free Horn expressions. This is done by using techniques for flattening and unflattening of examples and clauses, and a procedure for model finding for range restricted expressions. This procedure can also be used to solve the implication problem for this class. 1 Introduction We study the problem of exactly identifying universally quantified first order Horn expressions using Angluin's [Ang88] model of exact learning. Much of the work in learning theory has dealt with learning of Boolean exp...
The use of Background Knowledge in Inductive Logic Programming
, 1994
"... This report describes experiments in learning models for basic flight manoeuvres from behavioural traces of a human pilot when using a flight simulator. A first set of experiments using decision trees is presented. The autopilot built with the generated decision trees flies more smoothly than the h ..."
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Cited by 5 (3 self)
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This report describes experiments in learning models for basic flight manoeuvres from behavioural traces of a human pilot when using a flight simulator. A first set of experiments using decision trees is presented. The autopilot built with the generated decision trees flies more smoothly than the human pilot. However the results show also that propositional logiclevel representations, like decision trees, are inadequate to fully solve the problem. A learning system using a firstorder representation is required. However, current Inductive Logic Programming systems have severe limitations when dealing with such complex domains due to inefficiencies of searching large hypothesis spaces. An important issue to make the hypothesis space search tractable and efficient is the use of background knowledge. Some first results are reported based on a system under development that already shows some uses of background knowledge at a "local" level of learning a single predicate. Identification of...