Using generalization techniques to make AI systems more versatile

A group at DeepMind called the Open-Ended Learning Team has developed a new way to train AI systems to play games. Instead of exposing it to millions of prior games, as is done with other game playing AI systems, the group at DeepMind has given its new AI system agents a set of minimal skills that they use to achieve a simple goal (such as spotting another player in a virtual world) and then build on it. The researchers created a virtual world called XLand—a colorful virtual world that has a general video game appearance. In it, AI players, which the researchers call agents, set off to achieve a general goal, and as they do, they acquire skills that they can use to achieve other goals. The researchers then switch the game around, giving the agents a new goal but allowing them to retain the skills they have learned in prior games. The group has written a paper describing their efforts and have posted it on the arXiv preprint server.

from News on Artificial Intelligence and Machine Learning https://ift.tt/2TPkB01

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