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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 627 (58 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 136 (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.
OPUS: An efficient admissible algorithm for unordered search
 Journal of Artificial Intelligence Research
, 1995
"... OPUS is a branch and bound search algorithm that enables efficient admissible search through spaces for which the order of search operator application is not significant. The algorithm’s search efficiency is demonstrated with respect to very large machine learning search spaces. The use of admissibl ..."
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Cited by 76 (14 self)
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OPUS is a branch and bound search algorithm that enables efficient admissible search through spaces for which the order of search operator application is not significant. The algorithm’s search efficiency is demonstrated with respect to very large machine learning search spaces. The use of admissible search is of potential value to the machine learning community as it means that the exact learning biases to be employed for complex learning tasks can be precisely specified and manipulated. OPUS also has potential for application in other areas of artificial intelligence, notably, truth maintenance. 1.
Logic program specialisation through partial deduction: Control issues
 THEORY AND PRACTICE OF LOGIC PROGRAMMING
, 2002
"... Program specialisation aims at improving the overall performance of programs by performing source to source transformations. A common approach within functional and logic programming, known respectively as partial evaluation and partial deduction, is to exploit partial knowledge about the input. It ..."
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Cited by 53 (12 self)
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Program specialisation aims at improving the overall performance of programs by performing source to source transformations. A common approach within functional and logic programming, known respectively as partial evaluation and partial deduction, is to exploit partial knowledge about the input. It is achieved through a wellautomated application of parts of the BurstallDarlington unfold/fold transformation framework. The main challenge in developing systems is to design automatic control that ensures correctness, efficiency, and termination. This survey and tutorial presents the main developments in controlling partial deduction over the past 10 years and analyses their respective merits and shortcomings. It ends with an assessment of current achievements and sketches some remaining research challenges.
Some LowLevel Source Transformations for Logic Programs
 in Proceedings of Meta90 Workshop on Meta Programming in Logic
, 1990
"... This paper describes an algorithm performing an analysis and transformation of logic programs. The transformation achieves two goals: redundant functors are removed from the program, and procedures may be split into two or more specialised versions handling different cases. It can be applied to most ..."
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Cited by 30 (9 self)
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This paper describes an algorithm performing an analysis and transformation of logic programs. The transformation achieves two goals: redundant functors are removed from the program, and procedures may be split into two or more specialised versions handling different cases. It can be applied to most logic programming languages, including concurrent logic programming languages, because the transformations perform no unfolding of the program; they only remove some redundant operations within the unifications. The main saving is in heap usage, though time performance may also be improved. One of the main purposes of the transformation is to “clean up ” programs generated by other methods of transformation or synthesis. The analysis is an example of an abstract interpretation, and is guaranteed to terminate. A Prolog implementation of the algorithm, illustrating some metaprogramming techniques, is given and some results are reported.
Efficient Execution of HiLog in WAMbased Prolog implementations
 Proceedings on the twelth International Conference on Logic Programming
, 1995
"... In this paper we address the problem of efficiently implementing HiLog, a logic programming language with higherorder syntax and firstorder semantics. In contrast to approaches proposed in the literature that modify, or abandon the WAM framework in order to implement HiLog, our approach to the pro ..."
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Cited by 15 (4 self)
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In this paper we address the problem of efficiently implementing HiLog, a logic programming language with higherorder syntax and firstorder semantics. In contrast to approaches proposed in the literature that modify, or abandon the WAM framework in order to implement HiLog, our approach to the problem stems from a belief that the WAM should be an adequate abstract machine for the execution of any logic language with firstorder semantics. To show how to implement HiLog by staying within the WAM framework, we identify the reasons for poor performance characteristics of HiLog programs, present requirements for efficient HiLog execution, and propose a complete solution to the problem. Our proposal, which can be viewed either as a compiletime program specialisation preprocessing step, or as an enhancement to the HiLog encoding in predicate calculus presented by Chen, Kifer, and Warren in [1], allows HiLog to be efficiently implemented on any Prolog system by simply modifying Prolog's in...
Learning Decision Lists by Prepending Inferred Rules.
 In Proceedings of the Australian Workshop on Machine Learning and Hybrid Systems
, 1993
"... This paper describes a new algorithm for learning decision lists that operates by prepending successive rules to front of the list under construction. This contrasts with the original decision list induction algorithm which operates by appending successive rules to end of the list under construction ..."
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Cited by 11 (0 self)
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This paper describes a new algorithm for learning decision lists that operates by prepending successive rules to front of the list under construction. This contrasts with the original decision list induction algorithm which operates by appending successive rules to end of the list under construction. The new algorithm is demonstrated in the majority of cases to produce smaller classifiers that provide improved predictive accuracy than those produced by the original decision list induction algorithm.
Inductive Characterisation of Database Relations
, 1990
"... The general claims of this paper are twofold: there are challenging problems for Machine Learning in the field of Databases, and the study of these problems leads to a deeper understanding of Machine Learning. To support the first claim, we consider the problem of characterising a database relation ..."
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Cited by 10 (8 self)
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The general claims of this paper are twofold: there are challenging problems for Machine Learning in the field of Databases, and the study of these problems leads to a deeper understanding of Machine Learning. To support the first claim, we consider the problem of characterising a database relation in terms of highlevel properties, i.e. attribute dependencies. The problem is reformulated to reveal its inductive nature. To support the second claim, we show that the problems presented here do not fit well into the current framework for inductive learning, and we discuss the outline of a more general theory of inductive learning. KEYWORDS: Relational model, attribute dependencies, inductive learning, theoretical analysis. Contents 1. Introduction.......................................................................................................................1 2. Characterising a database relation..........................................................................................
Structured machine learning: the next ten years
, 2008
"... The field of inductive logic programming (ILP) has made steady progress, since the first ILP workshop in 1991, based on a balance of developments in theory, implementations and applications. More recently there has been an increased emphasis on Probabilistic ILP and the related fields of Statistic ..."
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Cited by 10 (2 self)
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The field of inductive logic programming (ILP) has made steady progress, since the first ILP workshop in 1991, based on a balance of developments in theory, implementations and applications. More recently there has been an increased emphasis on Probabilistic ILP and the related fields of Statistical Relational Learning (SRL) and Structured Prediction. The goal of the current paper is to consider these emerging trends and chart out the strategic directions and open problems for the broader area of structured machine learning for the next 10 years.