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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Cooperating with machines.

Jacob W Crandall¹, Mayada Oudah², Tennom³

¹Computer Science Department, Brigham Young University, 3361 TMCB, Provo, UT, 84602, USA. crandall@cs.byu.edu.

Nature Communications

|January 18, 2018

Summary

This summary is machine-generated.

This study introduces a new algorithm combining reinforcement learning with signaling mechanisms to achieve effective human-machine cooperation. The algorithm rivals human cooperation levels in complex games, demonstrating AI

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Area of Science:

Artificial Intelligence
Human-Computer Interaction
Game Theory

Background:

Historically, AI progress is measured by defeating humans in games, neglecting cooperative scenarios.
Human-machine cooperation is complex, especially when preferences are not fully aligned or conflicting.
Cooperation relies on factors beyond computation, including intuition, emotions, and social cues.

Purpose of the Study:

To develop an algorithm capable of beneficial human-machine cooperation in non-trivial scenarios.
To explore the integration of reinforcement learning with signaling mechanisms for cooperative AI.

Main Methods:

Developed a novel algorithm combining a state-of-the-art reinforcement-learning approach.
Integrated mechanisms for signaling to facilitate communication and cooperation.
Tested the algorithm in various two-player repeated stochastic games.

Main Results:

The developed algorithm demonstrated successful cooperation with both humans and other algorithms.
Cooperation levels achieved by the algorithm were comparable to human cooperation.
Effectiveness was shown across a variety of stochastic game scenarios.

Conclusions:

General human-machine cooperation is achievable through a combination of reinforcement learning and signaling.
Simple algorithmic mechanisms can facilitate complex cooperative behaviors.
This work opens new avenues for AI systems that can effectively collaborate with humans.