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Abstract
Collective decision-making (CDM) processes – wherein the knowledge of a group of individuals with a common goal must be combined to make optimal decisions – can be formalized within the framework of the deciding with expert advice setting. Traditional approaches to tackle this problem focus on finding appropriate weights for the individuals in the group. In contrast, we propose here meta-CMAB, a meta approach that learns a mapping from expert advice to expected outcomes. In summary, our work reveals that, when trying to make the best choice in a problem with multiple alternatives, meta-CMAB assures that the collective knowledge of experts leads to the best outcome without the need for accurate confidence estimates.
Original language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 113-124 |
Number of pages | 12 |
ISBN (Print) | 9783030630065 |
DOIs | |
Publication status | Published - Nov 2020 |
Event | International Conference on Computational Collective Intelligence - Da Nang, Viet Nam Duration: 30 Nov 2020 → 3 Dec 2020 Conference number: 12 https://iccci.pwr.edu.pl/2020/ |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12496 LNAI |
Conference
Conference | International Conference on Computational Collective Intelligence |
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Abbreviated title | ICCCI |
Country/Territory | Viet Nam |
City | Da Nang |
Period | 30/11/20 → 3/12/20 |
Internet address |
Keywords
- Collective decision-making
- Confidence
- Contextual bandits
- Deciding with expert advice
- Noise
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VLAAI1: Flanders Artificial Intelligence Research program (FAIR) – second cycle
1/01/24 → 31/12/28
Project: Applied
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EUAR39: H2020: TAILOR : Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization
1/09/20 → 31/08/24
Project: Applied