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Found 4 papers, 1 papers with code
A Data-Centric Behavioral Machine Learning Platform to Reduce Health Inequalities
Providing front-line health workers in low- and middle- income countries with recommendations and predictions to improve health outcomes can have a tremendous impact on reducing healthcare inequalities, for instance by helping to prevent the thousands of maternal and newborn deaths that occur every day.
Learning General Policies from Small Examples Without Supervision
It has been recently shown that these policies can be computed in two steps: first, a suitable abstraction in the form of a qualitative numerical planning problem (QNP) is learned from sample plans, then the general policies are obtained from the learned QNP using a planner.
Learning Features and Abstract Actions for Computing Generalized Plans
The actions in such plans, however, are not the actions in the instances themselves, which are not necessarily common to other instances, but abstract actions that are defined on a set of common features.
Combined Task and Motion Planning as Classical AI Planning
In this work, we show that it is possible to compile task and motion planning problems into classical AI planning problems; i. e., planning problems over finite and discrete state spaces with a known initial state, deterministic actions, and goal states to be reached.
