Wydawnictwa Uczelniane / TUL Press

Stały URI zbioruhttp://hdl.handle.net/11652/17

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AloneKnight – Enabling Affective Interaction within Mobile Video Games
(Wydawnictwo Politechniki Łódzkiej, 2023) Jemioło, Paweł; Świder, Krzysztof; Storman, Dawid; Adrian, Weronika T.
Artificial intelligence is used in various contexts, including video games, where it can enhance the game design and adapt content to players’ emotional states through affective computing. In this paper, we present an example of an affective mobile game and compare participants’ opinions after playing two versions of the game, with and without an affective loop. The game was developed using Unity. In the affective version, physiological data is recorded and analysed to detect emotions based on facial expressions and electrodermal activity, which then affects the game. The study with 11 participants showed positive feedback for the game with affective loop.
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.