Scalable Decision Making: Uncertainty, Imperfection, Deliberation, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013)

0396745 - ÚTIA 2014 RIV CZ eng G - Proceedings (international conference)
Guy, Tatiana Valentine (ed.) - Kárný, Miroslav (ed.) … Total 29 authors
Scalable Decision Making: Uncertainty, Imperfection, Deliberation, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013).
Prague: ÚTIA AV ČR, v.v.i, 2013. 112 s. ISBN 978-80-903834-8-7.
[European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDO 2013). Prague (CZ), 23.09.2013-27.09.2013]
R&D Projects: GA ČR GA13-13502S
Institutional support: RVO:67985556
Keywords : scalable * decision making * uncertainty * imperfection * deliberation
Subject RIV: BB - Applied Statistics, Operational Research

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.
Permanent Link: http://hdl.handle.net/11104/0224479