Cooperating with machines
Artificial intelligence is now superior to humans in many fully competitive games, such as Chess, Go, and Poker. Here the authors develop a machine-learning algorithm that can cooperate effectively with humans when cooperation is beneficial but nontrivial, something humans are remarkably good at.
Enregistré dans:
Auteurs principaux: | Jacob W. Crandall, Mayada Oudah, Tennom, Fatimah Ishowo-Oloko, Sherief Abdallah, Jean-François Bonnefon, Manuel Cebrian, Azim Shariff, Michael A. Goodrich, Iyad Rahwan |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2018
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/b4bb7dcd05cc4efe8f366dd9c3d888e9 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Algorithmic and human prediction of success in human collaboration from visual features
par: Martin Saveski, et autres
Publié: (2021) -
Publisher Correction: Algorithmic and human prediction of success in human collaboration from visual features
par: Martin Saveski, et autres
Publié: (2021) -
Universal resilience patterns in labor markets
par: Esteban Moro, et autres
Publié: (2021) -
Modularity and composite diversity affect the collective gathering of information online
par: Niccolò Pescetelli, et autres
Publié: (2021) -
Targeted social mobilization in a global manhunt.
par: Alex Rutherford, et autres
Publié: (2013)