Sign In

Communications of the ACM

ACM TechNews

AI Agents Learned Object Permanence by Playing Hide and Seek


View as: Print Mobile App Share: Send by email Share on reddit Share on StumbleUpon Share on Hacker News Share on Tweeter Share on Facebook
robot peeking from behind a wall

Credit: Getty Images

Researchers at the Allen Institute for AI (AI2) demonstrated that artificial intelligence agents learned the concept of object permanence — that objects hidden from view are still there — by playing hide and seek.

The agents, playing as both hiders and seekers, learned the game "Cache" via reinforcement learning. The agents began learning about the environment by taking random actions, like pulling on drawers, and dropping objects in random places. Their game play improved as they learned from outcomes, with the hider, for instance, learning that it had selected a good hiding place when the seeker failed to find the object.

Subsequent testing showed that the agents understood the principles of containment and object permanence and were able to rank images based on how much free space they contained.

The agents performed as well or better than models trained on the gold-standard ImageNet.

From IEEE Spectrum
View Full Article

 

Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA


 

No entries found

Sign In for Full Access
» Forgot Password? » Create an ACM Web Account