From “how” to learn to “what” to learn in multiagent systems and robotics

There remains much more to Artificial Intelligence than understanding how to learn with neural networks. Inspired in part by how babies, and humans in general, tend to choose for themselves what (and when ) to learn in addition to how to learn, this article discusses similar opportunities, in the areas of multiagent systems and robotics. Grounded in past research on the topic, it categorizes opportunities for choosing what to learn as coming from task decomposition or simplification for difficult problems and/or large environments; unsupervised pre‐training of representations and priors; multiagent learning; and learning from human interaction. The article is based closely on a keynote talk with the same title, delivered at the AAAI‐2026 conference in Singapore on January 22nd, 2026.