A system for maintaining user’s engagement in computer games

V magistrskem delu predstavimo sistem za ohranjanje uporabnikovega sodelovanja pri igranju računalniških iger, ki deluje na podlagi merjenja psihofizičnih značilk, kot so povprečni srčni utrip, galvanski odziv kože in električna aktivnost v možganih. Pokazati želimo, da je ročno prilagajanje karakteristik računalniških iger možno prepustiti avtomatiziranemu sistemu za ohranjanje uporabnikove vključenosti. Implementacijo takšnega sistema smo razdelili na dva funkcijska sklopa. Prvi sklop predstavlja model vključenosti igralca, ki na podlagi karakteristik igranja in izmerjenih psihofizičnih značilk, ocenjuje uporabnikovo trenutno vključenost. Model uporabnikove vključenosti smo zgradili z uporabo metod nadzorovanega strojnega učenja. Drugi sklop sistema predstavlja algoritem, ki ob zaznanem padcu vključenosti igralca spreminja karakteristike igre z namenom ohranjati uporabnikovo vključenost. Za učenje optimalne strategije spreminjanja karakteristik igre smo uporabili spodbujevano učenje.In our master thesis, we present a system for maintaining a user\u27s engagement in computer games based on measured psychophysical indicators such as average heartbeat, galvanic skin response and electric activity in the brain. We wish to prove that hand adjustment of video game parameters can instead be performed by an automated system for maintaining player engagement. We split the implementation of such a system into two separate sections. The first section consists of a user-engagement model that predicts the current user engagement based on the player\u27s gameplay characteristics and measured psychophysical indicators. We have built the engagement model using supervised machine learning techniques. The second section of the system is represented by an algorithm that, when a drop in user engagement is detected, adjusts game parameters in order to maintain user engagement. The optimal strategy for changing game parameters was learned through the use of reinforcement learning