Artificial intelligence research community and associations in Poland

In last years Artificial Intelligence presented a tremendous progress by offering a variety of novel methods, tools and their spectacular applications. Besides showing scientific breakthroughs it attracted interest both of the general public and industry. It also opened heated debates on the impact of Artificial Intelligence on changing the economy and society. Having in mind this international landscape, in this short paper we discuss the Polish AI research community, some of its main achievements, opportunities and limitations. We put this discussion in the context of the current developments in the international AI community. Moreover, we refer to activities of Polish scientific associations and their initiative of founding Polish Alliance for the Development of Artificial Intelligence (PP-RAI). Finally two last editions of PP-RAI joint conferences are summarized

Rough sets analysis of diagnostic capacity of vibroacoustic symptoms

AbstractThe paper refers to the problem of diagnostic classification of mechanical objects using vibroacoustic symptoms. A new approach based on the rough sets theory is applied to evaluate the symptoms from the point of view of their diagnostic capacity, i.e., the quality of estimation of a technical state of a mechanical object. The approach enables reduction of the set of symptoms to a minimal subset ensuring a satisfactory estimation. The minimal subset is then used to create a classifier of a technical state. Particular attention is paid to a comparison of different methods of calculation of symptom limit values which divide domains of symptoms into intervals corresponding to classes of technical states. The analysed set of data concerns the technical state of rolling bearings installed in a laboratory stand. They are described by a set of symptoms which result from measurements of noise and vibration of bearing housings. The bearings are in good or bad technical states. The paper presents particular steps of the rough sets methodology and gives, as a final result, a classifier of a technical state of bearings based on a minimal subset of symptoms with the greatest diagnostic capacity