{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/45887"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/45887","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Learning for informative path planning","abstract":"Through the combined use of regression techniques, we will learn models of the uncertainty propagation efficiently and accurately to replace computationally intensive Monte- Carlo simulations in informative path planning. This will enable us to decrease the uncertainty of the weather estimates more than current methods by enabling the evaluation of many more candidate paths given the same amount of resources. 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