This NSF Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) Project Grant, awarded to Arizona State University in the amount of $598,123 on July 15, 2024, will develop new algorithms to enable AI systems to autonomously learn hierarchical world models and high-level actions. The goal is to create AI systems, such as hospital robots and disaster-recovery support systems, that can plan reliably and efficiently to accomplish complex user-desired tasks, without requiring extensive hand-engineering by domain experts. The research will pursue two broad approaches: bottom-up abstractions to learn abstractions based on unannotated behavior in training scenarios, and top-down abstractions to selectively increase the resolution of an uninformed, coarse abstract representation. The resulting algorithms, benchmarks, test scenarios, and software will be made broadly available to the research community in open-source format.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $598.1k | 7/12/24 |