4 papers
Benchmarking World-Model Learning with Environment-Level Queries
Archana Warrier, Dat Nguyen, Michelangelo Naim +8
World models are central to building AI agents capable of flexible reasoning and planning. Yet current evaluations (i) test only properties measurable from observed interactions, s…
ExoPredicator: Learning Abstract Models of Dynamic Worlds for Robot Planning
Yichao Liang, Dat Nguyen, Cambridge Yang +7
Long-horizon embodied planning is challenging because the world does not only change through an agent's actions: exogenous processes (e.g., water heating, dominoes cascading) unfol…
Assessing Adaptive World Models in Machines with Novel Games
Lance Ying, Katherine M. Collins, Prafull Sharma +11
Human intelligence exhibits a remarkable capacity for rapid adaptation and effective problem-solving in novel and unfamiliar contexts. We argue that this profound adaptability is f…
Combining Induction and Transduction for Abstract Reasoning
Wen-Ding Li, Keya Hu, Carter Larsen +11
When learning an input-output mapping from very few examples, is it better to first infer a latent function that explains the examples, or is it better to directly predict new test…