guest ::
| Home > Conference materials > Proceedings > Scalable Decision Making: Uncertainty, Imperfection, Deliberation, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013) |
Original title:
Scalable Decision Making: Uncertainty, Imperfection, Deliberation, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013)
Authors: Guy, Tatiana Valentine ; Kárný, Miroslav
Document type: Proceedings
Conference/Event: 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
Year: 2013
Language: eng
Abstract: 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.
Keywords: decision making; deliberation; imperfection; scalable; uncertainty
Project no.: GA13-13502S (CEP)
Funding provider: GA ČR
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at the institute of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0224479
Permalink: http://www.nusl.cz/ntk/nusl-156617
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Proceedings
Authors: Guy, Tatiana Valentine ; Kárný, Miroslav
Document type: Proceedings
Conference/Event: 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
Year: 2013
Language: eng
Abstract: 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.
Keywords: decision making; deliberation; imperfection; scalable; uncertainty
Project no.: GA13-13502S (CEP)
Funding provider: GA ČR
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at the institute of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0224479
Permalink: http://www.nusl.cz/ntk/nusl-156617
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Proceedings
Record created 2013-10-04, last modified 2021-11-24