The paper introduces the ParkinsonCheck application. It is an app for smart phones based on spirography (spiral drawing) intended to detect signs of Parkinson's disease (PD) and essential tremor (ET), which is the main differential diagnosis from PD in the early stage of the disease. The app is equipped with an expert system and is the first such app to be completely automated. Its intended use is twofold: (a) to act as a standalone test for general population, advising potential patients to seek medical help as early as possible, and (b) to be used by neurologists as a portable and inexpensive fully digitalised clinical decision support system. ParkinsonCheck is currently freely available in Slovenia on four mobile platforms as a pilot study. After potentially upgrading its expert system with new learning data, the plan is for it to be translated into English and offered worldwide.

We present a novel approach to embodied learning of qualitative models. We introduce algorithm STRUDEL that enables an autonomous robot to discover new concepts by performing experiments in its environment. The robot collects data about its actions and its observations of the environment. From the obtained data, the robot learns qualitative descriptive models of the effects that its actions have in the environment. Models are learned using inductive logic programming. We describe two experiments with a humanoid robot Nao in which Nao learns descriptive qualitative models which contain what can be interpreted as simple definitions of the concepts of movability and stability.

In this paper we discuss the application of a new machine learning approach - argumentation based machine learning - to the legal domain. Argumentation based machine learning is particularly suited to law as it makes use of the justifications of decisions to guide its learning. Importantly, where a large number of decided cases are available, it provides a way of identifying which need to be considered, so that only decisions which will have an influence are examined.