Cooperating with machines

Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human–machine cooperation is beneficial but non-trivial, such as sc...

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Bibliographic Details
Published in:Nature communications Vol. 9; no. 1; pp. 233 - 12
Main Authors: Crandall, Jacob W., Oudah, Mayada, Tennom, Ishowo-Oloko, Fatimah, Abdallah, Sherief, Bonnefon, Jean-François, Cebrian, Manuel, Shariff, Azim, Goodrich, Michael A., Rahwan, Iyad
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 16.01.2018
Nature Publishing Group
Nature Portfolio
Subjects:
639/705/117
706/689/2788
Algorithms
Artificial Intelligence
Chess
Communication
Computer applications
Computer Science
Cooperation
Cooperative Behavior
Data Structures and Algorithms
Emotions
Human-Computer Interaction
Humanities and Social Sciences
Humans
Machine Learning
multidisciplinary
Norms
Science
Science (multidisciplinary)
Signaling
Stochastic Processes
ISSN:2041-1723, 2041-1723
Online Access:Get full text
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Summary:Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human–machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions. Here, we develop an algorithm that combines a state-of-the-art reinforcement-learning algorithm with mechanisms for signaling. We show that this algorithm can cooperate with people and other algorithms at levels that rival human cooperation in a variety of two-player repeated stochastic games. These results indicate that general human–machine cooperation is achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms. 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.
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PMCID: PMC5770455
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-017-02597-8