This paper firstly provides a re-appraisal of the development of techniques for inverting deduction, secondly introduces Mode-Directed 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 re-assessment of previous techniques in terms of inverse entailment leads to new results for learning from positive data and inverting implication between pairs of clauses.

Inductive Logic Programming (ILP) is a research area which investigates the construction of first-order definite clause theories from examples and background knowledge. ILP systems have been applied successfully in a number of real-world domains. These include the learning of structureactivity rules for drug design, finite-element mesh design rules, rules for primary-secondary prediction of protein structure and fault diagnosis rules for satellites. There is a well established tradition of learning-in-the-limit results in ILP. Recently some results within Valiant's PAC-learning 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 general-purpose, efficient approach to predicate invention is demonstrated. ILP is underconstrained by its logical ...

This thesis is about inductive learning, or learning from examples. The goal has been to investigate ways of improving learning algorithms. The chess end-game "King and Rook against King" (KRK) was chosen, and a number of benchmark learning tasks were defined within this domain, sufficient to over-challenge stateof -the-art learning algorithms. The tasks comprised learning rules to distinguish (1) illegal positions and (2) legal positions won optimally in a fixed number of moves. From our experimental results with task (1) the best-performing algorithm was selected and a number of improvements were made. The principal extension to this generalisation method was to alter its representation from classical logic to a non-monotonic formalism. A novel algorithm was developed in this framework to implement rule specialisation, relying on the invention of new predicates. When experimentally tested this combined approach did not at first deliver the expected performance gains due to restrictio...

Inductive logic programming (ILP) is built on a foundation laid by research in other areas of machine learning and computational logic. But in spite of this strong foundation, at just over 10 years of age ILP now faces a number of new challenges brought on by exciting areas of application. Research in other areas of machine learning and computational logic can contribute much to help ILP meet these challenges. After a brief review, the paper presents ve future research directions for ILP and points to initial approaches or results where they exist. It is hoped that the paper will motivate research workers in machine learning and computational logic to invest some time into ILP.

Inductive logic programming (ILP) is built on a foundation laid by research in other areas of computational logic. But in spite of this strong foundation, at 10 years of age ILP now faces a number of new challenges brought on by exciting application opportunities. The purpose of this paper is to interest researchers from other areas of computational logic in contributing their special skill sets to help ILP meet these challenges. The paper presents five future research directions for ILP and points to initial approaches or results where they exist. It is hoped that the paper will motivate researchers from throughout computational logic to invest some time into "doing" ILP.

Inductive logic programming (ILP) is a research area which has its roots in inductive machine learning and computational logic. The paper gives an introduction to this area based on a distinction between two different semantics used in inductive logic programming, and illustrates their application in knowledge discovery and programming. Whereas most research in inductive logic programming has focussed on learning single predicates from given datasets using the normal ILP semantics (e.g. the well known ILP systems GOLEM and FOIL), the paper investigates also the non-monotonic ILP semantics and the learning problems involving multiple predicates. The non-monotonic ILP setting avoids the order dependency problem of the normal setting when learning multiple predicates, extends the representation of the induced hypotheses to full clausal logic, and can be applied to different types of application. Keywords: inductive logic programming, induction, logic programming, machine learning 1 Intro...

We describe the principles and functionalities of Dlab (Declarative LAnguage Bias), which is an algorithm for defining syntactically and traversing efficiently hypothesis spaces in the context of concept learning and knowledge discovery tasks. Though Dlab is designed for first-order languages it can also be used to constrain propositional concept spaces. In an appendix we document a Dlab Prolog library available via anonymous ftp. The WWW-homepage of Dlab can be found at URL http : ==www:cs:kuleuven:ac:be=cwis=research=ai=Research=dlab \Gamma E:shtml Keywords : declarative language bias, machine learning, knowledge discovery 1 Introduction Concept learning algorithms in general demand the syntactic delineation of a language L in which to search for the target concept. Even if we choose the search space L to be finite, it is in most cases impractical to define L extensionally. We then need a formalism to formulate an intensional syntactic definition of language L. The problem of m...

The task of predicate invention in Inductive Logic Programming is to extend the hypothesis language with new predicates, in case the vocabulary given initially is insufficient for the learning task. Introducing new predicates involves searching for an appropriate argument structure. In this paper we investigate the problem of choosing arguments for a new predicate. We identify the relevant terms to be considered as arguments, and propose methods to choose among them based on propositional minimisation. 1 Introduction The aim of inductive learning is to hypothesize a general rule from specific examples. The success of this task strongly depends on the appropriate representation for both examples and rules. A rule that is too complex to learn in one representation might be easily found by the learning system in another. In order to reduce the effort in tailoring the representation to the requirements of the learning procedure, the idea of adjusting the representation automatically has c...

This paper addresses the problem of improving the representation space in a rule-based intelligent system, through exception-based learning. Such a system generally learns rules containing exceptions because its representation language is incomplete. However, these exceptions suggest what may be missing from the system's ontology, which is the basis of the representation language. We describe an interactive exception-based learning method for eliciting new elements in the system's ontology in order to eliminate the exceptions of the rules. This method is implemented in the Disciple learning agent shell and has been evaluated in an agent training experiment at the US Army War College. 1

While computational logic has become widely used for representing and reasoning with linguistic knowledge, the cross-fertilization between logic programming and machine learning has given rise to a new discipline known as inductive logic programming. Inspired by, and building on the achievements of logic programming within both natural language research and machine learning, we point out opportunities for induction of linguistic knowledge within logic (programming). Keywords: inductive logic programming, natural language processing, logic programming, machine learning. 1 Introduction There is a growing interest amongst both linguistic engineers and machine learning researchers for applying symbolic learning algorithms in natural language R&D 1 . Linguists confronted with the high cost of development of essential resources are drawn towards machine learning in search for generic technologies to exploit corpora for system training purposes. Vice versa machine learning researchers are...