0396745 20240103203024.020131003235959.9 978-80-903834-8-7 Prague ÚTIA AV ČR, v.v.i 2013 112 s. P scalable decision making uncertainty imperfection deliberation cav_un_auth*0101092 Guy Tatiana Valentine Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101124 Kárný Miroslav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. GA13-13502S GA ČR cav_un_auth*0292725 Machine learning (ML) and knowledge discovery both use and serve to decision making (DM), which has to cope with uncertainty, incomplete knowledge, problem and data complexity and imperfection (limited cognitive and evaluating capabilities) of the involved heterogeneous multiple participants (aka agents, decision makers, components, controllers, classifiers, etc.). Contemporary DM deals with complex systems characterised by heterogeneous components and their goal-motivated dynamic interactions. The individual participants are selfish, i.e. follow their individual goals. There is no well-justified way to influence or describe the resulting collective behaviour of such a system via a well-proved combination of the selfish components. Economic and natural sciences describe concepts governing the functioning of systems of selfish participants as well as ways influencing their behaviour. However, the majority of solutions rely on the human moderator/manager controlling such a system. cav_un_auth*0294508 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDO 2013) Prague 23.09.2013-27.09.2013 CZ 2014 BB 29 RVO:67985556 http://hdl.handle.net/11104/0224479 2013 2013 Prague ÚTIA AV ČR, v.v.i 978-80-903834-8-7