During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not so, later definitions in the 1990s, such as Mitchell's, tended to use a one-dimensional approach to Machine Learning based solely on predictive accuracy, ultimately favouring statistical over symbolic Machine Learning approaches. In this paper we provide a definition of comprehensibility of hypotheses which can be estimated using human participant trials. We present two sets of experiments testing human comprehensibility of logic programs. In the first experiment we test human comprehensibility with and without predicate invention. Results indicate comprehensibility is affected not only by the complexity of the presented program but also by the existence of anonymous predicate symbols. In the second experiment we directly test whether any state-of-the-art ILP systems are ultra-strong learners in Michie's sense, and select the Metagol system for use in humans trials. Results show participants were not able to learn Editors: James Cussens and Alessandra Russo. Mach Learn (2018Learn ( ) 107:1119Learn ( -1140 the relational concept on their own from a set of examples but they were able to apply the relational definition provided by the ILP system correctly. This implies the existence of a class of relational concepts which are hard to acquire for humans, though easy to understand given an abstract explanation. We believe improved understanding of this class could have potential relevance to contexts involving human learning, teaching and verbal interaction.

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract. During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not so, later definitions in the 1990s, such as that of Mitchell, tended to use a one-dimensional approach to Machine Learning based solely on predictive accuracy, ultimately favouring statistical over symbolic Machine Learning approaches. In this paper we provide a definition of comprehensibility of hypotheses which can be estimated using human participant trials. We present the results of experiments testing human comprehensibility of logic programs learned with and without predicate invention. Results indicate that comprehensibility is affected not only by the complexity of the presented program but also by the existence of anonymous predicate symbols. Permanent repository link

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In diesem Beitrag stellen wir ein computerbasiertes Selbst-Sehtestgerät vor, das unter anderem die Prüfung des Nah-und Fernvisus erlaubt. Das Gerät arbeitet mit getrennter Bddaufbereitung für das linke und das rechte Auge. Das System erlaubt die Bestimmung von Visuswerten zwischen 0,1 und 1,6 auf beliebige, im Bereich von 0,35 m bis 6 m liegende Distanzen. Außer der Visusbestimmung sind mit dem Gerät auch Färb-, Phorie-, Stereopsis-und Kontrasttests möglich. Erste Einsätze in der Industrie haben gezeigt, daß das Testgerät praktikabel ist.We describe a computer-aided self-test vision screener for testing near and far visual acuity. The device generates images separately for each eye on a LCD. Acuity can be measured m the range between 0.1 and 1.6 for any distance between 0.34 m and 6 m. In addition, it also enables testing for colour deficiency, phoria, stereopsis and contrast sensitivity. The device is fully automatic and enables self-testing of the above mentioned functions. Initial practical application in an industrial environment has demonstrated the practicability of the device.