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DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes
We introduce the DAG-GP model, which linearly combines latent Gaussian processes so that MOGP outputs follow a directed acyclic graph structure.
RADMPC: A Fast Decentralized Approach for Chance-Constrained Multi-Vehicle Path-Planning
This approach is intractable as fleet size increases because computation time is exponential with respect to the number of vehicles being planned over due to a polynomial increase in coupling constraints between vehicle pairs.
Vulcan: A Monte Carlo Algorithm for Large Chance Constrained MDPs with Risk Bounding Functions
Chance Constrained Markov Decision Processes maximize reward subject to a bounded probability of failure, and have been frequently applied for planning with potentially dangerous outcomes or unknown environments.
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