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Found 3 papers, 1 papers with code
Uncertain multi-agent MILPs: A data-driven decentralized solution with probabilistic feasibility guarantees
We consider uncertain multi-agent optimization problems that are formulated as Mixed Integer Linear Programs (MILPs) with an almost separable structure.
Sampling-based optimal kinodynamic planning with motion primitives
Nodes are progressively added to the tree of feasible trajectories in the RRT* algorithm by extracting at random a sample in the gridded state space and selecting the best obstacle-free motion primitive in the database that joins it to an existing node.
Robotics
Randomised Algorithm for Feature Selection and Classification
The selection method progressively refines a probability distribution defined on the model structure space, by extracting sample models from the current distribution and using the aggregate information obtained from the evaluation of the population of models to reinforce the probability of extracting the most important terms.
