Abstract  A new research area, Inductive Logic Programming, is presently emerging. While inheriting various positive characteristics of the parent subjects of Logic Programming and Machine Learning, it is hoped that the new area will overcome many of thelimitations of its forebears. The background to present developments within this area is discussed and various goals and aspirationsfor the increasing body of researchers are identified. Inductive Logic Programming needs to be based on sound principles fromboth Logic and Statistics. On the side of statistical justification of hypotheses we discuss the possible relationship betweenAlgorithmic Complexity theory and Probably-Approximately-Correct (PAC) Learning. In terms of logic we provide a unifying frameworkfor Muggleton and Buntine’s Inverse Resolution (IR) and Plotkin’s Relative Least General Generalisation (RLGG) by rederivingRLGG in terms of IR. This leads to a discussion of the feasibility of extending the RLGG framework to allow for the inventionof new predicates, previously discussed only within the context of IR.


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