Representation Learning for Acting and Planning (RLeap)
Two of the main research threads in AI revolve around the development of data-based learners capable of inferring behavior and functions from experience and data, and model-based solvers capable of tackling well-defined but intractable models like SAT, classical planning, and Bayesian networks. Learners, and in particular deep learners, have achieved considerable success but result in black boxes that do not have the flexibility, transparency, and generality of their model-based counterparts. Solvers, on the the hand, require models which are hard to build by hand. The RLeap project aims at achieving an integration of learners and solvers in the context of planning by addressing the problem of learning first-order planning representations from raw perceptions alone without using any prior symbolic knowledge. The ability to construct first-order symbolic representations and using them for expressing, communicating, achieving, and recognizing goals is a main component of human intelligence and a fundamental, open research problem in AI. By addressing and solving this problem, the project can make a difference in how general, explainable, and trustworthy AI can be understood and achieved. Read more about the project here.
PI: Hector Geffner
Core team: RLeap team
Funding: RLeap is partially funded by an ERC Advanced Grant (1 October 2020 - 30 September 2025).