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.

Knowledge elicitation is known to be a difficult task and thus a major bottleneck in building a knowledge base. Machine learning has long ago been proposed as a way to alleviate this problem. Machine learning usually helps the domain expert to uncover some of the more tacit concepts. However, the learned concepts are often hard to understand and hard to extend. A common view is that a combination of a domain expert and machine learning would yield the best results. Recently, argument based machine learning (ABML) has been introduced as a combination of argumentation and machine learning. Through argumentation, ABML enables the expert to articulate his knowledge easily and in a very natural way. ABML was shown to significantly improve the comprehensibility and accuracy of the learned concepts. This makes ABML a most natural tool for constructing a knowledge base. The present paper shows how this is accomplished through a case study of building a knowledge base of an expert system used in a chess tutoring application.

Recently we showed that under very reasonable conditions, incomplete, real-time search methods like RTA*work better with pessimistic heuristic functions than with optimistic, admissible heuristic functions of equal quality. The use of pessimistic heuristic functions results in higher percentage of correct decisions and in shorter solution lengths. We extend this result to learning RTA*(LRTA*) and demonstrate that the use of pessimistic instead of optimistic (or mixed) heuristic functions of equal quality results in much faster learning process at the cost of just marginally worse quality of converged solutions.

The study verifies a hypothesis that GDV in fact measures vitality. For that purpose a limited study was performed and the findings support the hypothesis. However, further investigation is needed in order to more reliably confirm it.