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The clausal discovery engine Claudien is presented. Claudien is an inductive logic programming engine that fits in the knowledge discovery in databases and data mining paradigm as it discovers regularities that are valid in data. As such Claudien performs a novel induction task, which is called characteristic induction from closed observations, and which is related to existing formalizations of induction in logic. In characterising induction from closed observations, the regularities are represented by clausal theories, and the data using Herbrand interpretations. Claudien also employs a novel declarative bias mechanism to define the set of clauses that may appear in a hypothesis. Keywords : Inductive Logic Programming, Knowledge Discovery in Databases, Data Mining, Learning, Induction, Semantics for Induction, Logic of Induction, Parallel Learning. 1 Introduction Despite the fact that the areas of knowledge discovery in databases [Fayyad et al., 1995] and inductive logic programmin...

We present positive PAC-learning results for the nonmonotonic inductive logic programming setting. In particular, we show that first order range-restricted clausal theories that consist of clauses with up to k literals of size at most j each are polynomialsample polynomial-time PAC-learnable with one-sided 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 non-trivial models and propose a way to avoid these problems using a representation change called flattening. Finally, we compare our results to PAC-learnability results for the normal inductive logic programming setting. 1

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 ...

. 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...

Editor: M. des Jardins and D. Gordon Abstract. A comparative study is presented of language biases employed in specific-to-general 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 well-formed 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.

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...