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63
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.
Theory completion using Inverse Entailment
, 2000
"... The main realworld applications of Inductive Logic Programming (ILP) to date involve the "Observation Predicate Learning" (OPL) assumption, in which both the examples and hypotheses define the same predicate. However, in both scientific discovery and language learning potential applications exist i ..."
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Cited by 49 (23 self)
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The main realworld applications of Inductive Logic Programming (ILP) to date involve the "Observation Predicate Learning" (OPL) assumption, in which both the examples and hypotheses define the same predicate. However, in both scientific discovery and language learning potential applications exist in which OPL does not hold. OPL is ingrained within the theory and performance testing of Machine Learning. A general ILP technique called "Theory Completion using Inverse Entailment" (TCIE) is introduced which is applicable to nonOPL applications. TCIE is based on inverse entailment and is closely allied to abductive inference. The implementation of TCIE within Progol5.0 is described. The implementation uses contrapositives in a similar way to Stickel's Prolog Technology Theorem Prover. Progol5.0 is tested on two different datasets. The first dataset involves a grammar which translates numbers to their representation in English. The second dataset involves hypothesising the fu...
Probabilistic Logic Learning
 ACMSIGKDD Explorations: Special issue on MultiRelational Data Mining
, 2004
"... The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning. A rich variety of di#erent formalisms and learning techniques have been developed. This pap ..."
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Cited by 34 (8 self)
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The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning. A rich variety of di#erent formalisms and learning techniques have been developed. This paper provides an introductory survey and overview of the stateof theart in probabilistic logic learning through the identification of a number of important probabilistic, logical and learning concepts.
Learning Concepts from Sensor Data of a Mobile Robot
 Machine Learning
, 1996
"... . Machine learning can be a most valuable tool for improvingthe flexibility and efficiency of robot applications. Many approaches to applying machine learning to robotics are known. Some approaches enhance the robot's highlevel processing, the planning capabilities. Other approaches enhance the low ..."
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Cited by 32 (6 self)
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. Machine learning can be a most valuable tool for improvingthe flexibility and efficiency of robot applications. Many approaches to applying machine learning to robotics are known. Some approaches enhance the robot's highlevel processing, the planning capabilities. Other approaches enhance the lowlevel processing, the control of basic actions. In contrast, the approach presented in this paper uses machine learning for enhancing the link between the lowlevel representations of sensing and action and the highlevel representation of planning. The aim is to facilitate the communication between the robot and the human user. A hierarchy of concepts is learned from route records of a mobile robot. Perception and action are combined at every level, i.e., the concepts are perceptually anchored. The relational learning algorithm grdt has been developed which completely searches in a hypothesis space, that is restricted by rule schemata, which the user defines in terms of grammars. Keywords...
An Efficient Subsumption Algorithm for Inductive Logic Programming
 IN PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING
, 1994
"... In this paper we investigate the efficiency of ` subsumption (` ` ), the basic provability relation in ILP. As D ` ` C is NPcomplete even if we restrict ourselves to linked Horn clauses and fix C to contain only a small constant number of literals, we investigate in several restrictions of D. ..."
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Cited by 31 (3 self)
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In this paper we investigate the efficiency of ` subsumption (` ` ), the basic provability relation in ILP. As D ` ` C is NPcomplete even if we restrict ourselves to linked Horn clauses and fix C to contain only a small constant number of literals, we investigate in several restrictions of D. We first adapt the notion of determinate clauses used in ILP and show that `subsumption is decidable in polynomial time if D is determinate with respect to C. Secondly, we adapt the notion of klocal Horn clauses and show that ` subsumption is efficiently computable for some reasonably small k. We then show how these results can be combined, to give an efficient reasoning procedure for determinate klocal Horn clauses, an ILPproblem recently suggested to be polynomial predictable by Cohen (1993) by a simple counting argument. We finally outline how the `reduction algorithm, an essential part of every lgg ILPlearning algorithm, can be improved by these ideas.
Declarative Bias in ILP
, 1996
"... . Interest in Declarative bias in Machine Learning is growing with the expressivity of the concept description language of ML systems. Inductive Logic Programming more than any other ML field is thus concerned with explicitely biasing learning. The main issues already identified in declarative bias ..."
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Cited by 27 (1 self)
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. Interest in Declarative bias in Machine Learning is growing with the expressivity of the concept description language of ML systems. Inductive Logic Programming more than any other ML field is thus concerned with explicitely biasing learning. The main issues already identified in declarative bias [RG90] have been studied within the ILP project, i.e. the restriction of the size of the search space for the target concept and representation of the bias. As a first step, an extensive study of existing ILP systems and the elicitation of the role of hidden bias has led to define typologies of bias in relation with their effects on the learning process as well as alternative representation for bias. Declarative representations of bias have been defined through different types of languages so that bias can be easily set and shifted. In parallel with the definition, the representation and the experimentation of various biases, the interactions between different types of bias have been analyze...
Declarative Bias for SpecifictoGeneral ILP Systems
 Machine Learning
, 1995
"... Editor: M. des Jardins and D. Gordon Abstract. A comparative study is presented of language biases employed in specifictogeneral learning systems within the Inductive Logic Programming (ILP) paradigm. More specifically, we focus on the biases employed in three well known systems: CLINT, GOLEM and ..."
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Cited by 24 (8 self)
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Editor: M. des Jardins and D. Gordon Abstract. A comparative study is presented of language biases employed in specifictogeneral learning systems within the Inductive Logic Programming (ILP) paradigm. More specifically, we focus on the biases employed in three well known systems: CLINT, GOLEM and ITOU, and evaluate both conceptually and empirically their strengths and weaknesses. The evaluation is carried out within the generic framework of the NINA system, in which bias is a parameter. Two different types of biases are considered: syntactic bias, which defines the set of wellformed clauses, and semantic bias, which imposes restrictions on the behaviour of hypotheses or clauses. NINA is also able to shift its bias (within a predefined series of biases), whenever its current bias is insufficient for finding complete and consistent concept definitions. Furthermore, a new formalism for specifying the syntactic bias of inductive logic programming systems is introduced.
Inductive Logic Programming for Natural Language Processing
 IN MUGGLETON, S. (ED.), INDUCTIVE LOGIC PROGRAMMING: SELECTED PAPERS FROM THE 6TH INTERNATIONAL WORKSHOP
, 1997
"... This paper reviews our recent work on applying inductive logic programming to the construction of natural language processing systems. We have developed a system, Chill, that learns a parser from a training corpus of parsed sentences by inducing heuristics that control an initial overlygenera ..."
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Cited by 23 (1 self)
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This paper reviews our recent work on applying inductive logic programming to the construction of natural language processing systems. We have developed a system, Chill, that learns a parser from a training corpus of parsed sentences by inducing heuristics that control an initial overlygeneral shiftreduce parser. Chill learns syntactic parsers as well as ones that translate English database queries directly into executable logical form. The ATIS corpus of airline information queries was used to test the acquisition of syntactic parsers, and Chill performed competitively with recent statistical methods. English queries to a small database on U.S. geography were used to test the acquisition of a complete natural language interface, and the parser that Chill acquired was more accurate than an existing handcoded system. The paper also includes a discussion of several issues this work has raised regarding the capabilities and testing of ILP systems as well as a summary of our current research directions.
Applications of a Logical Discovery Engine
 IN PROCEEDINGS OF THE AAAI WORKSHOP ON KNOWLEDGE DISCOVERY IN DATABASES
, 1994
"... The clausal discovery engine claudien is presented. claudien discovers regularities in data and is a representative of the inductive logic programming paradigm. As such, it represents data and regularities by means of first order clausal theories. Because the search space of clausal theories is larg ..."
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Cited by 21 (5 self)
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The clausal discovery engine claudien is presented. claudien discovers regularities in data and is a representative of the inductive logic programming paradigm. As such, it represents data and regularities by means of first order clausal theories. Because the search space of clausal theories is larger than that of attribute value representation, claudien also accepts as input a declarative specification of the language bias, which determines the set of syntactically wellformed regularities. Whereas other papers on claudien focuss on the semantics or logical problem specification of claudien, on the discovery algorithm, or the PAClearning aspects, this paper wants to illustrate the power of the resulting technique. In order to achieve this aim, we show how claudien can be used to learn 1) integrity constraints in databases, 2) functional dependencies and determinations, 3) properties of sequences, 4) mixed quantitative and qualitative laws, 5) reverse engineering, and 6) classificati...
Constraint Inductive Logic Programming
, 1996
"... . This paper is concerned with learning from positive and negative examples expressed in firstorder logic with numerical constants. The presented approach is based on the cooperation of Inductive Logic Programming (ILP) and Constraint Logic Programming (CLP), and proceeds as follows: ffl A discrim ..."
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Cited by 20 (6 self)
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. This paper is concerned with learning from positive and negative examples expressed in firstorder logic with numerical constants. The presented approach is based on the cooperation of Inductive Logic Programming (ILP) and Constraint Logic Programming (CLP), and proceeds as follows: ffl A discriminant induction problem is shown to be equivalent to a Constraint Satisfaction Problem (CSP): all constrained clauses covering positive examples and rejecting negative examples can be trivially derived from the solutions of this CSP. ffl Solving this CSP then allows to build the G set of solutions in terms of Version Spaces; this resolution can be delegated to a constraint solver. ffl This CSP provides a tractable computational characterization of G, which is sufficient to classify further examples and offers simple countingbased heuristics to resist noisy data. In this hybrid ILPCLP approach, CLP performs most of the search involved in inductive learning; the advantage is to benefit fro...