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  • Pozycja
    Brief Overview of Selected Research Directions and Applications of Process Mining in KRaKEn Research Group
    (Wydawnictwo Politechniki Łódzkiej, 2023) Kluza, Krzysztof; Zaremba, Mateusz; Sepioło, Dominik; Wiśniewski, Piotr; Adrian, Weronika T.; Gaudio, Maria Teresa; Jemioło, Paweł; Adrian, Marek; Jobczyk, Krystian; Ślażyński, Mateusz; Stachuta-Terlecka, Bernadetta; Ligęza, Antoni
    Process mining allows for exploring processes using data from event logs. By providing insights into how processes are actually executed, rather than how they are supposed to be executed, process mining can be used for optimizing business processes and improving organizational efficiency. In this exploratory paper, we report on selected research threads related to process mining carried out within KRaKEn Research Group at AGH University of Science and Technology. We introduce a collection of initial ideas that require further exploration. Our research threads are concerned with the use of process mining techniques 1) for discovering processes from unstructured data, specifically text from e-mails, 2) for explaining black-box machine learning models, using process models as a global explanation, and 3) for analyzing data from different food industry systems to identify inefficiencies and provide recommendations for improvement.
  • Pozycja
    A Comparison of Shallow Explainable Artificial Intelligence Methods against Grammatical Evolution Approach
    (Wydawnictwo Politechniki Łódzkiej, 2023) Sepioło, Dominik; Ligęza, Antoni
    This paper reports on an ongoing, innovative research in the area of eXplainable Artificial Intelligence (XAI). An XAI task is considered as finding an explanation of the model generated via Machine Learning by identifying the most influential variables for local decision-making. The proposed approach moves the explanatory process to a new, deeper-level dimension. It is oriented towards Model Discovery, i.e. the internal structure and functions of the components. An experiment on Function Discovery via Grammatical Evolution is reported in brief.