Název: Scalable Decision Making: Uncertainty, Imperfection, Deliberation, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013)
Autoři: Guy, Tatiana Valentine ; Kárný, Miroslav
Typ dokumentu: Sborníky
Konference/Akce: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDO 2013), Prague (CZ), 2013-09-23 / 2013-09-27
Rok: 2013
Jazyk: eng
Abstrakt: 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.
Klíčová slova: decision making; deliberation; imperfection; scalable; uncertainty
Číslo projektu: GA13-13502S (CEP)
Poskytovatel projektu: GA ČR

Instituce: Ústav teorie informace a automatizace AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný v příslušném ústavu Akademie věd ČR.
Původní záznam: http://hdl.handle.net/11104/0224479

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-156617


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